Patent Application
20180081330
The present invention relates to a method and apparatus for controlling an environment management system within a building.
Buildings are at least as highly individual as people. Although there are classifications of dwellings by archetypes, there are at least a hundred of these. It is also clear that many members of these archetypical groups differ significantly from each other because of differences in construction details, later modifications and different states of repair. Building systems and, in particular, heating and ventilating systems also vary widely and the interaction between these systems and the buildings they are used in is complex.
Furthermore, identical buildings with different occupants can have quite different energy behaviours. Moving an individual into a different group of occupants in a different building also causes significant changes of behaviour. Although it is clear that the behaviour of a boiler is intimately linked to the other components in the heating system as well as the building and the occupants, it may not be so immediately obvious that the energy use of a freezer depends on where it is located in a building and the physics of the micro-environment around the condenser especially.
The application of sophisticated control to buildings and especially domestic buildings has been quite limited to date and especially in respect of incorporating building physics models into the control scheme. With the advent of more affordable and powerful home automation components there is now an opportunity to implement more effective control. Generically, building physics models form part of the set of functions of a Building Environment Management System (BEMS). However, the opportunity for the present invention is most significant in Home Environment Management Systems (HEMS). For the sake of brevity, the remainder of the specification therefore tends to refer to HEMS only. It should, however, be understood that the invention can be applied to any BEMS, not just a HEMS.
The current state of the art includes: a) multi-zonal control of heating systems through a central controller connected to individual room thermostats; b) detection of high heat inputs and low rates of heating signalling open windows and avoiding waste by reducing heating; c) estimation of the time required to heat a zone in order to schedule a target temperature by time rather than a heating on/off control.
While each of the above systems can be superior to timed operation of a heating system with individual zonal thermostats (for example, Thermostatic Radiator Valves TRVs), they fail to address some important requirements relating to building heating and thermal dynamics.
The present invention has therefore been designed with the foregoing in mind.
Some examples of factors that current systems fail to adequately address are as follows:
Generically, the building physics module of a HEMS can also provide inputs to other processes, for example the likely cost impact of changing set-points in different zones or identifying the likely causes of major building fabric inefficiencies.
A potential approach to building physics is to use detailed modelling tools to calculate the building response, based on a very accurate description of the building and its components. This method has been used for a small number of buildings as part of a design-build-verify approach to building physics and efficiency. For large commercial buildings with major environmental control problems this may be a cost-effective way of dealing with loss of rental value and amenity through offline modelling and design modifications. However the level of expertise, sophistication and data required to produce a faithful model of a building in this way is very high and quite beyond what could be achieved for a dwelling. Not only is the cost-benefit ratio unacceptable for dwellings but the model is harder to achieve through less access to as-built data, greater exposure of the zones to external environmental effects and more complex and variable patterns of occupation than a hotel or office block. The applicants therefore propose a solution to this problem.
According to a first aspect of the present invention there is provided a method of operating an environment management system within a building as defined in claim 1. The method comprises: for each of at least a first and a second model:
Based on the determined uncertainty bands, the method selects a control strategy that minimises a likely level of deviation from the desired future system state, the control strategy comprising control parameters for the environment management system, and operates the environment management system to control the environment in the building in accordance with the selected control strategy.
Embodiments of the first aspect of the invention therefore enable model-based predictive control of a HEMS while minimising the risk of dissatisfaction due to inaccuracies in the modelling. The method provides a way of introducing a cautious approach into the control strategy. By running each of the two types of models and comparing them with historical data from previous time periods and model predictions, a range, or band, of uncertainty as to the likely accuracy of the model predictions can be established. This then allows a “safe” strategy to be determined based on minimising the likelihood of the strategy leading to a condition of the building (system state) that is too far away from the desired condition.
According to a second aspect of the present invention there is defined a method of operating an environment management system within a building as defined in claim 1. The method comprises:
Embodiments of the second aspect of the invention enable model-based predictive control of a HEMS with greater reliability than in prior art systems due to the evaluation of two different types of models to determine the one that is most likely to give the best results for a given set of circumstances and to control the HEMS on that basis. It is believed that the hybrid approach of the present invention will enable the control system to account for inevitable errors and unknown variables in the dynamic operation of the building. Embodiments of the invention may comprise predicting values of future environmental variables and possible future control inputs.
Embodiments of both the first and second aspects of the present invention may be performed by a building physics module/unit which forms a part of an environment management system. The building physics module may form an integral part of the system or may be a discrete unit.
The methods may comprise the construction of plausible hypotheses about the building physics (i.e. thermal dynamics) based on inspection by an experienced installation engineer and/or user input. Support tools may be provided to aid the collection of appropriate building data. The data collection may be influenced by data collected from other similar buildings containing environment management systems. For example, the system may determine that for certain types of buildings, certain data is more important than other data. The hypotheses may be used to predict a building response to control and other factors (i.e. weather).
The methods may comprise a setup/installation process that attempts to identify major appliances and their location within the building and to use the social interaction between the installer and building occupants to configure the system to suit their needs. The setup process may also comprise inputting data relating to the structure and properties of the building (for example, including room layout or functional zones, location and properties of windows, doors, radiators etc.). Embodiments of the invention may be designed to maximise the utility of the information collected during system installation and throughout the lifetime operation of the HEMS.
The first model may comprise one or more sub-models selected from a stock of building sub-system models, which may be selected by an installation engineer upon system installation, or may be identified by the system in light of data input by the installation engineer.
The methods may further comprise determining a minimum set of parameters that can provide an effective representation of a building. A relatively long time series of actual data from one or more buildings may be analysed to make this determination.
The methods may further comprise tuning the model parameters until the model explains the measured system state (i.e. represents the building's thermal dynamics). This may comprise modelling dynamics of one or more building systems (e.g. heating, ventilation, hot water systems) and its interaction with the physics of the building (e.g. location of windows).
The methods may comprise a training period to identify appropriate component models and their parameters. The training period may be segregated by characteristic parameters. In other words, the sub-models (equations and parameters) may be classified according to environmental factors that have been shown to discriminate between the utility of models in general in other similar buildings and, specifically, historically in the present building. For example, the segregation may be by when the building is occupied, when the building is occupied but all are asleep, when the building is unoccupied, by external temperature level or time of day relative to sunrise/sunset. It should be understood that any such segregation may be quite coarse, but multi-dimensional.
The segregation may have a dimensionality that is reduced by Principal Component Analysis to identify periods which genuinely produce distinct sets of model and parameter representations. Additional dimensions of model training period segregation may be identified from historic data and/or by adding additional measured and estimated parameters from other buildings as putative dimensions. Analysis of time period characteristic dimensions across multiple environment management systems in buildings of similar use (dwellings, offices, leisure centres etc.) may be used to seed or initiate the segregation process.
The training period may comprise simplifying the first model by grouping some of the terms together (e.g. when the system is identified as being over-parameterised).
The first model may comprise use of Continuous Time Stochastic Models.
The second model may comprise a set of sub-models developed based on segregation by the same environmental factors as referred to above. The identification of sub-models may be developed using actual measured data.
The second model may comprise an Artificial Neural Network. It should be noted that the second model is an implicit (black box) model of the system whereas the first model (which is a parameterised physical model) may be considered as a quasi-explicit (grey box) model of the system.
It should be noted that references to the first and second models do not imply a temporal order to the method but simply denote two different types of model.
The methods may comprise identifying hidden state variables (for example, door and window opening, blind raising and lowering, internal temperature of walls and windows, the temperature of radiators, curtain opening or closing, air exchange (i.e. ventilation) rates) in the first model and/or sub-models and may comprise hypothesising a probable state of said hidden state variables in a predetermined time period. Such hypotheses may be used within control algorithms to deliver comfort parameters such as air temperature, humidity levels and ventilation control.
The sub-models offered for selection by an installation engineer may be chosen or ordered to reflect model structures and/or parameter probability distributions that have been determined to be most successful across multiple (e.g. similar) environment management systems.
The sub-models may be configured to receive inputs from other parts of the environment management system, which may enable estimates to be made of physics inputs such as human metabolic heat input, heat gains from appliances, secondary heating (e.g. from towel rails, showering or bathing), forced ventilation systems, dehumidifiers and humidity sources such as washing, drying and cooking.
The steps of evaluating the predictions of the first and second models may comprise comparing model outputs and/or energy inputs.
The steps of controlling the environment management system may comprise controlling one or more of: a heating system, a hot water system, a ventilation system and a cooling system to achieve the desired future system state.
The system may be controlled to manage one or more of heat, humidity, condensation and mould.
The methods may further comprise using parameters from either or both of the first and second models in functions other than direct control of the environment management system. Such functions may comprise one or more of: budget management, appliance selection, home improvement advice, estimating inherent building efficiencies, providing evidence to support social payments, targeting sales of products and services etc.
In embodiments of the second aspect, if, under certain circumstances (i.e. for a particular segregation), one of the first or second models is determined to generally always be selected, the system may adapt and may always use that model without evaluating the other model.
In embodiments of the invention, a central server may be provided to gather data from a plurality of HEMS. In which case, the central server will be able to build up an extremely valuable database of properties of buildings in different areas. Furthermore, data gathered across a large stock of buildings may enable the construction of a set of models and parameters that will have a high chance of working effectively in a new building in a relatively short time-scale (i.e. out of the box).
In accordance with a third aspect of the invention, there is provided an apparatus for controlling an environment management system within a building, comprising:
In accordance with a fourth aspect of the invention, there is provided a building environment management system comprising the apparatus according to the second aspect.
The third and fourth aspects of the invention may comprise any of the features described above in relation to the first and second aspects of the invention.
According to a fifth aspect of the present invention there is provided an apparatus for operating an environment management system within a building, comprising: apparatus for measuring a current system state; and a processor configured to, for each of at least a first and a second model:
Based on the determined uncertainty bands, the processor selects a control strategy that minimises a likely level of deviation from the desired future system state, the control strategy comprising control parameters for the environment management system. The processor operates the environment management system to control the environment in the building in accordance with the selected control strategy.
In accordance with a six aspect of the invention, there is provided building environment management system comprising the apparatus according to the fifth aspect.
The fifth and sixth aspects of the invention may comprise any of the features described above in relation to the first and second aspects of the invention.
Embodiments of the invention will now be described, by way of example only, with reference to the Figures of the accompanying drawings in which:
An embodiment of the present invention will now be described in the context of a building physics module (BPM) within a Home Environment Management System (HEMS), as a specific case of a Building Environment Management System (BEMS).
The purpose of this particular BPM/system is to predict, in real time, the temperature and heating demands of each room in a home. More precisely, the home will be split into thermal zones, each with their own sensors. The module will resolve the data for temperatures and recommended heating demands in each zone and will provide control signals to action the recommended heating levels.
In principle, the models described can be used to estimate the heat inputs required to reach a future desired state or the future state that would be reached with a particular set of heat inputs. In some embodiments, both functions may be required for effective control optimisation. Thus, the BPM of some embodiments constitutes an apparatus for operating an environment management system within a building and comprises: apparatus for measuring a current system state; and a processor configured to: use a first model to predict, for a chosen time period ahead, one or both of: i) control requirements for the environment management system in light of the current measured system state and a desired future system state or ii) a future system state that would be reached with a particular set of control inputs; wherein the first model comprises a parameterised physical model of the building; use a second model to predict, for the chosen time period ahead, one or both of: i) control requirements for the environment management system in light of the current measured system state and the desired future system state or ii) a future system state that would be reached with a particular set of control inputs; wherein the second model comprises an implicit (black box) model of the building; evaluate the predictions of the first and second models based on prior success at predicting the building's thermal behaviour for conditions similar to conditions that are forecast for the chosen time period ahead; select one of the first and second models, based on the evaluation; and operate the environment management system to control the environment in the building in accordance with the selected model for the chosen time period.
The BPM of some embodiments constitutes an apparatus for operating an environment management system within a building, comprising: apparatus for measuring a current system state; and a processor configured to, for each of at least a first and a second model: A. use the model to predict, for a chosen time period ahead, one or both of: i) control requirements for the environment management system in light of the current measured system state and a desired future system state or ii) a future system state that would be reached with a particular set of control inputs; wherein the first model comprises a parameterised physical model of the building; B. evaluate the predictions of the first and second models based on prior success at predicting the building's thermal behaviour for conditions similar to conditions that are forecast for the chosen time period ahead; and C. determine a band of uncertainty for the desired or predicted future system state. Based on the determined uncertainty bands, the processor selects a control strategy that minimises a likely level of deviation from the desired future system state, the control strategy comprising control parameters for the environment management system. The processor operates the environment management system to control the environment in the building in accordance with the selected control strategy.
Installation and Setup
The thermal zones will be defined during installation of the system by the installation engineer, who will assign a zone to each individually heated space. Each space may be thermally-coupled to one or more other heated spaces, in which case these connections should also be input to the system by the engineer, so that the BPM can resolve the associated heat exchanges. The engineer will be provided with tools that enable a spatial model of the building and its contents and appliances to be constructed, which will support a number of HEMS functions, including the BPM.
The BPM may require the engineer to assign and dimension components such as doors, windows, radiators etc. and to identify features such as blinds, curtains canopies, as well as sub-systems such as the heating and water system, extractor fans and hoods and any other features that could contribute to the generation and transfer of heat and moisture within, into and out of the building, including storage reservoirs, such as the thermal capacity of walls and radiators or the location of soft furnishings, towels etc.
System Overview
The present embodiment illustrates how the invention can be implemented in respect of heating control in a building using Continuous Time Stochastic Models and Artificial Neural Networks. It should be clear to one skilled in the art where Bayesian Statistics, Principal Component Analysis and analytical techniques for similarity and congruence in multi-dimensional space (for example Euclidian separation and morphological categorisation) can be applied. How these concepts could be extended to include humidity or extract hidden state estimations (i.e. a door has been closed) from the models is not explicitly discussed but will be clear from the overall teaching of this specification and the particular exemplification that follows.
Two different types of model are utilised in the present BPM: the first model comprises Artificial Neural Networks (ANN) and the second model comprises Continuous Time Stochastic Models (CTSM). In some embodiments the system uses predictions of both models and evaluates the predictions based on prior success at predicting the building's thermal behaviour for conditions similar to conditions that are forecast. The system then determines a band of uncertainty for the desired or predicted future system state and selects a control strategy that minimises a likely level of deviation from the desired future thermal state of the building. In other embodiments the system selects the most appropriate model to use for each 24 hour period, based on its prior success at predicting the buildings' thermal behaviour for conditions similar to those which are forecast for the day ahead.
ANN is a ‘black box’ model, such that its internal processes have no physical meaning. This can be an advantage as it can model phenomena that are unknown or that cannot be defined physically, as long as there is a proxy input for the description of such phenomena.
CTSM, on the other hand, is more flexible as it has physical meaning. This ‘grey box’ model also requires considerably less data to produce reliable results and can explicitly model changes in the envelope of the building (i.e. due to a window being opened), including hidden state variables.
The first model (CTSM) may require a training period (Process I) step 12 that requires a cumulative period of around three heating season days, during which time the building is unoccupied and the exterior envelope is sealed (e.g. windows and external doors are closed). This may require an elapsed time of as much as, say, two weeks. This may be sufficient to define a model that describes the thermal performance of the building, with all significant terms of thermal resistance, capacitance and heat gain characterised, in the case of a sealed envelope. A further period of up to eighteen weeks may be required to define a cumulative period of a further three days where, for each zone, the envelope is not sealed (i.e. a door or window is open). Under these conditions modified resistance terms can be estimated, to account for the reduced thermal resistance due to heat transfer through open windows/doors.
With the two CTSM models calibrated (envelope sealed and envelope open) it is straightforward to determine occasions during which a transition from the ‘sealed’ model to the ‘open’ model—and vice versa—is required using the accumulated historical data (e.g. obtained over the training period). This and subsequent data can be used to fit a model to predict the associated transition probabilities, using a binomial family of generalised linear models, as will be described in more detail below. This model can later be used in conjunction with weather forecast and/or occupancy forecast data from other modules to stochastically predict whether windows will be open or closed and thus to select the appropriate CTSM model for each forecast.
The second ‘black box’ ANN model requires no such separation—it implicitly handles both ‘sealed’ and ‘open’ envelope cases. In this case, the model may require an initial training period of at least two weeks' (up to six weeks′) continuous data as per step 14 (Process IIA) in which to configure an approximate model, and with which initial predictions may be made. However, a further eight to sixteen week period of ‘on-the-fly’ continuous training may be required as per step 16 (Process IIB), during which the network is continually updated.
After the training periods described above are complete, a normal run-time (Process III) step 18 comprises model prediction, evaluation and selection. Model prediction may output temperature and heating demand predictions for both model types, the evaluation stage tests these against observed data and the selection process determines which of the ANN and CTSM models is best suited for each forecast day.
The following sections describe in detail the processes involved in each stage of the flowchart in
ANN Description
Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired by the functioning of the brain and biological neural networks. They are statistical learning algorithms that allow the construction of mathematical models based on historical observed data, finding relationships between large numbers of parameters. They are thus, classified as black-box models, where the behaviour of certain properties or variables of a system may be estimated depending on a given stimulus, without describing the physical or mathematical processes taking place.
A neural network is a system where nodes, called neurons, are interconnected and distributed in layers in such a way that, from a specific configuration of inputs, an output response can be obtained. The way in which this response is calculated depends on the mathematical activation function used in order to compute the output from the inputs.
The architecture of the network can be defined by the number and types of layers. Commonly, these are one input layer, one or more hidden layers and one output layer.
The performance of ANNs relies on the quantity and quality of the data used to train the network. An appropriate training period is crucial to ensure that the network is reliably configured with a view to reliably forecasting future system behaviour: in this case room air temperature and heating demands.
The network configuration described below has been found successful in balancing accuracy and complexity. It is a Feed Forward Network with three layers: input, hidden and output. However, other types of network, or, indeed, other types of implicit (e.g. time-series) model may be employed in other embodiments.
The input and output layers in the present embodiment are linear layers, whereas the hidden layer follows a sigmoid function (another common possibility is a hyperbolic tangent function). A bias neuron is included in both the input and output layer; this is an extra weighting parameter that improves the learning process of the network by allowing modifications to the activation function as necessary.
In the present case, the input layer comprises the following neurons, for time step t:
The output layer comprises:
The hidden layer contains 1.5 times the number of input neurons. For example, in the case of N=4 thermal zones, the input layer will have 18 neurons, the hidden layer will have 27 hidden neurons and the output layer will have 8 neurons (describing the temperature and heat flux outputs for each of the four zones).
Through the weighting parameters that are given to the connections between their neurons ANNs are implicitly capable of describing behavioural influences such as the opening of windows and their impacts on thermal performance and the use of appliances such as gas rings. In fact, the applicants have found that explicit representation of such effects, requiring Boolean variables, tends to destabilise ANNs.
Measurements internal to the building are derived from measurement sensors. A horizontal irradiance sensor may be required to be installed on the roof of the building in which the HEMS is installed, so that local reflecting occlusions to sky and sun are directly represented. Such a sensor should be capable of calculating a split between global (Igh) and diffuse horizontal irradiance (Idh). Given a calculated solar altitude (γ) for the relevant time and location, the beam normal irradiance (Ibn) is then simply: Ibn=(Igh−Idh)/sin γ and the incident direct solar irradiance I(t) [W·m−2] is: Ibn cos θ, where θ is the angle of incident on the receiving plane (i.e. the window).
It should also be noted that an ANN could be trained to predict local direct horizontal irradiance given the coincident horizontal irradiance measured at a local meteorological station, so that weather forecasts for that station could be localised (indeed this principle could also be applied to other meteorological parameters), for example, the average wind speed and direction. These parameters are also likely to be required for aspects of the second CTSM model described below.
For the purposes of this embodiment, we have assumed that irradiance will need to be measured on each dwelling, whereas wind speed and direction can be estimated from external inputs. Clearly, there is an opportunity to use data across multiple HEMS to improve local estimates of meteorological data and forecasts in combination with other weather data and forecasting.
ANN: Initial Training
ANNs generally need a large amount of data before they start making sensible predictions. For that reason, an initial period of around two weeks is likely to be required to collect data and train the initial network, which will then be subsequently refined and used in the present method.
The initial training period for the ANN is described in
Step 20. Collect data for 2 weeks (e.g. using a data sampling rate of 5 minutes)
Step 21. Create a data set using the data gathered from two weeks of operation. The data set may comprise the following inputs and outputs:
Step 22. Initialize neural network (configured with layers and neurons as described above).
Step 23. Train network during at least 100 epochs (i.e. for t=1 to 100).
Step 24. This results in an initial network(0).
ANN: ‘On-The-Fly’ Continuous Training
After the first two weeks of data is collected and used to train and obtain network(0), a process of dynamic training is implemented. For each 15-minute time step during the next ten weeks, new data is measured and used to retrain the network. The updated network is accepted only when it leads to improved prediction capabilities when compared to network(0).
For each time step, the training process is as described in
Step 30. At time t, new measured data is recorded, and can be used to evaluate the predictions made at the last time step. The first step is therefore to read the measured values at t for:
Step 32. Using both the current measurements of zone conditions (T_zone(t) and Qh_zone(t)) and the prediction of current zone conditions that was calculated at the previous time step P[T_zone(t)] and P[Qh_zone(t)], evaluate the quality of each prediction by calculating the Mean Squared Error, MSE(t), for all zones.
Step 34. Evaluate the performance of the last network used network(t−1) in comparison with the previous network(t−2), by calculating the MSE(t) of the predictions. If the MSE(t) is less than or equal to that calculated for the previous timestep MSE(t−1), network(t−1) is retained (step 36), else network(t−1) is rejected in favour of network(t−2) (step 38). The chosen network is re-named network_old(t).
Step 40. At this point Qh_zone(t), T_zone(t) are measured and known. These values are incorporated into the network_old(t) as target output values. Input values will be the measured values at time t−1. These values therefore constitute an input-output pair that the network can use to retrain:
Step 42. The network is retrained with the above data and saved as network_new(t). Step 44a. The next predicted output values for zone temperature P[T_zone(t+1)] and heating P[Qh_zone(t+1)] are forecast using Network_new(t)
Step 44b. The next predicted output values for zone temperature P[T_zone(t+1)] and heating P[Qh_zone(t+1)] are also forecast using Network_old(t)
Step 46. The previous two steps produce two sets of predictions, one for each network (old and new). In order to choose one, the network that predicts the lowest temperature difference (between the prediction and the current temperature) or its MSE will be selected and saved as network(t). This stage avoids predicting large temperature changes in the zones.
The process of
CTSM Description
Continuous time stochastic modelling (CTSM) is a process used to solve Stochastic Differential Equations (SDEs). In contrast with traditional Ordinary or Partial Differential Equations (ODEs or PDEs), SDEs can explicitly represent processes that are stochastic in nature. In other words they express randomness due, for example, to thermophysical properties that vary with moisture content, to infiltration that varies with local pressure fluctuations etc. and other processes that affect the dynamic behaviour of a building. Stochastic terms within SDEs are generally random white noise or a derivative of Brownian motion. A Wiener process is the continuous time stochastic process used in an SDE to represent Brownian motion.
CTSM has thus far been used to model the dynamic thermal behaviour of simple mono-zone unoccupied buildings. Embodiments of the present invention extend far beyond the current literature in its aim of modelling a multi-zone home, explicitly accounting for thermal interactions between these zones, and also accounting for occupants' interactions with the home (in particular, with respect to internal heat gains and interactions with envelope openings such as doors and windows). The model can also be adapted to account for seasonal factors and, for example, for variations in the effective solar aperture of windows as nearby obstructions occlude views to the sun during periods of low solar altitude.
CTSM involves estimating the parameters of and then solving a system of stochastic differential equations for each zone (room) within the home. Such systems of equations will be largely similar in structure; their differences will lie in the parameter values that reflect variations in the size and configuration of rooms, the degree of solar exposure, the magnitude of internal heat gains etc. While the systems are inextricably linked they are solved independently, as interactions affecting a zone are resolved for within the system of equations for that zone.
Within each system of equations there are as many equations as there are state variables. These are the key variables that must be predicted in order to model some effect (not all modelled phenomena require state variables). The first and most important state variable is Ti, the internal temperature of the zone. Other equations generally lead on from this and describe the relation between Ti and themselves. Equation [1] below illustrates a basic form of Ti with respect to time t:
in which:
Equation [1] can be extended to represent other phenomena, for example, to handle interactions with adjacent zones by adding the following additional term:
where:
In the present case, a new state variable for the temperature in the adjacent zone may be required.
Other internal gains may also be added, but without the need for a further state variable, as this simply requires that an additional term of the form below to be included within the equation for Ti:
where φp is the incidental internal heat gain within the zone.
A more realistic form of Ti based on the above additions is then as per Equation [2] below:
The summation term represents all adjacent zones (i.e. with N adjacent zones there are N terms for thermal exchange between these and the target zone). Rn is the equivalent of the Rint above. In this example the state variables are:
The thermal gain term φh is in the state variable equation for Th in this case to allow Th to have a resistance and its own capacitance.
This formulation represents the most demanding case of four state variables depending on adjacent zones. Additional state variables can be added, but these have been found to bring diminishing returns in terms of predictive power, to reduce the likelihood of convergence in the estimation of parameter values and to increase run time.
CTSM-r is a module selected from a statistical computing package R that has been used to estimate the parameters of the above SDEs. There are three model structures to choose from in CTSM-r: linear time-invariant, linear time-variant and non-linear. The equations shown above are linear time-invariant. Given a choice of model structure CTSM-r has three parameter estimation techniques: maximum likelihood, maximum a posteriori and using multiple independent datasets.
Maximum likelihood estimation (ML) estimates parameters that will maximise the likelihood function of a sequence of measurements. The likelihood function L is the joint probability density p as per Equation [3] below:
L(θ;YN)=p(YN|θ) Eq. [3]
where θ are the parameters and YN is the sequence of measurements (i.e. the training data for the model). This process selects the parameters most likely to output predictions matching the measured values (training data).
Maximum a posteriori estimation (MAP) is similar to ML but can take advantage of prior information about the parameters. The new probability density function p, in this case, is as per Equation [4] below:
It should be noted that with no prior information MAP reduces to the ML estimation, so that ML is a special case of the MAP estimation. Further, multiple independent data sets is a generalisation of MAP estimation, where the expression for the probability density function in MAP is expanded for multiple consecutive measurements. In the present embodiment MAP estimation is employed but in other embodiments other techniques may be used.
CTSM Modelling
As with ANN, CTSM involves both training and modelling processes. In this particular case, we will consider three distinct training processes of the CTSM model. The first two relate to the estimation of parameters describing the envelope (and within this stage we can select the most efficient form of CTSM model), whilst the latter models occupants' interactions with the envelope, which determines which of the former models should be selected at a given time step.
A period of equivalent continuous data relating to the envelope being either sealed or open is required for training. Notably, this period does not have to be actually continuous; it can consist of separate periods of data spliced together. A usable dataset may be obtained from between around 3 days to a week and the CTSM will estimate the following parameters in the state equation (e.g. Equation [2]):
Next, the desired state variable (Ti) is calculated using the predicted parameters through a forward selection procedure, where models are fitted using a maximum a posteriori estimation of the parameters. This process is illustrated in
Selection indicators are used to evaluate and compare the current model versus each of the extended models (step 56) and the possible candidate models for improvement that are selected in each iteration are the smallest extensions to the current model. The procedure stops when no extensions to the model yield a p-value of less than 5% or when the quotient of the change in AIC becomes insignificant (step 58). The p-value is an estimate of the probability that the prediction could have arisen by chance if a null hypothesis were true—i.e. that observations and predictions could be from the same dataset. If the p-value is below the stated significance level (i.e. p<5%) the null hypothesis can be rejected since the two datasets are significantly different from one another. The second criterion exists to ensure that a more complex model is not selected if it insignificantly improves results. The threshold and criteria can of course be adjusted at the modeller's discretion. The selected extended model is evaluated (step 56) and if the result is satisfactory the model is kept and the next iteration can be started, otherwise the previous step 54 is used again to select another extension. During this process each model should also be assessed for the quality of the predictions, for example, based on the following:
Once the form of model has been selected in accordance with
Ti may now be calculated for an occupied building following the above procedure.
As noted earlier, with the two CTSM models (envelope sealed and envelope open) calibrated after the training period it is straightforward to determine occasions during which a transition from the ‘sealed’ model to the ‘open’ model—and vice versa—is required using the accumulated historical data; based on the model that returns the smallest predictive error. This and subsequent data can be used to fit a model to predict the associated transition probabilities, using the binomial family of generalised linear models. This model can later be used in conjunction with weather forecast data to stochastically predict whether windows will be open or closed and thus to select the appropriate CTSM model for these forecasts.
Thus, once we have the following three parameterised models:
We can use the three models during operation of the system (if CTSM is selected for the present day's control) as follows:
Reverse Process (Heating Prediction)
As already noted, it may be important to be able to predict the heating load required to maintain a given target temperature. This can be achieved through a further hybrid model (based on CTSM plus regression). Following results from CTSM for the predicted indoor temperature (as described above), regression analysis can be used to predict heating loads based on the predicted temperature and relevant predictors (e.g. internal temperatures, solar heat gains and internal heat gains).
Regression analysis relates a dependent variable to one or more independent variables. In this case, the dependent variable is φh whereas candidate independent variables include:
Model Prediction, Evaluation and Selection
The BPM described herein can therefore predict temperatures and heating demands using the two approaches detailed above (ANN and CTSM). In some embodiments, for a given day (say from midnight to midnight) the most appropriate model can be selected, on the basis of past success for the upcoming typology of day, based on weather forecasts for the day. This process will improve over time as the amount of historic performance data increases and the method progressively refines the classification of the models for each typology of day: CTSM or an ANN model.
The typology of the day may take the form of an n-dimensional matrix of occupational and climatic parameters, with the following candidate dimensions:
By default, all elements of this n-dimensional matrix of typologies of day will contain references to CTSM, which will be used to inform heating control actions during the initial training period. Subsequently the matrix will be progressively refined, with elements referencing the CTSM (now trained for this particular building, for the open and sealed cases and predicting which of these applies to each timestep for each zone) or ANN variant that minimises MSE.
In general, the algorithm may work as follows:
Operation/Control of the System
In the present case, the steps are as follows:
Step 100. At time t, new measured data is recorded, and can be used to evaluate the predictions made at the last time step. The first step is therefore to read the measured values at t for:
Step 102. The actual measurements of zone conditions at time t and for the last 24 hours up to t−24 (T_zone(t, . . . , t−24) and Qh_zone(t, . . . , t−24)) are then compared with the predicted zone conditions P[T_zone(t, . . . , t−24)] and P[Qh_zone(t, . . . , t−24)], for the same period in order to evaluate the quality of each prediction by calculating the Mean Squared Error, MSE, of the prediction. This produces an error for the last 24 hours (step 104).
Step 106. The system checks whether it is the beginning of the day.
Step 108. If it is not, the ANN is used to predict the zone conditions for the next 24 hours P[T_zone(t, . . . , t+24)] and P[Qh_zone(t, . . . , t+24)] using network(t−1).
Step 110. If it is the beginning of the day, the system uses the data from the last day to retrain the ANN model.
Step 112. If the error for the retrained model is greater than the error for the previous (old) model, the model is not updated and is designated as the revised network.
Step 114. If the error for the retrained model is less than the error for the previous (old) model, the model is updated to the retrained model and is designated as the revised network.
Step 116. The revised network is then used in step 108 to predict the zone conditions for the next 24 hours P[T_zone(t, . . . , t+24)] and P[Qh_zone(t, . . . , t+24)]. The process of
In some embodiments the BPM may be used in a manner that minimises the risk of dissatisfaction of one or more occupants of the building arising from poor or inaccurate model predictions. This introduces a cautious approach into the control strategy. In these embodiments each of the models is run in the manner described above, but instead of simply selecting the one best model for the control strategy, an approach is used based on the results of the predictions of both (or all) models and a comparison with historical data to determine a level of uncertainty as to how well each of the models is likely to perform.
Accordingly, as in the embodiments described above, each of the models is used to predict, for a chosen time period ahead, one or both of: i) control requirements for the environment management system in light of a current measured system state and a desired future system state or ii) a future system state that would be reached with a particular set of control inputs; wherein the first model comprises a parameterised physical model of the building and the second model comprises an implicit model of the building. As in other embodiments the predictions of the models are evaluated based on prior success at predicting the building's thermal behaviour for conditions similar to conditions that are forecast for the chosen time period ahead. In these embodiments, a range or band of uncertainty is determined for each of the model predictions, to determine what is described hereafter as an uncertainty space.
For example, the uncertainty space may be defined in terms of probabilities. Based on the historical data for use of one model, a statistical distribution of the outcomes of a control strategy can be determined for a parameter, for example the temperature of a room or zone. This can be used as the basis of a probability distribution for the control strategy based on that model's predictions—e.g. probabilities that the temperature deviates from the desired temperature at a certain time by more than a specified number of degrees. This can also be done for different factors that may give rise to the uncertainty—i.e. factors that may contribute to the model getting the prediction wrong, such as uncertainty about the forecast weather. A similar analysis can be carried out for the other model (or models). The uncertainty space therefore represents a distribution of probabilities associated with each of models, based on a statistical analysis of historical data and model predictions.
Based on the determined uncertainty space, a control strategy is selected that minimises a likely level of deviation from the optimum or desired thermal condition of the building. This then allows a “safe” strategy to be selected based on minimising the likelihood of the strategy leading to a condition of the building that is too far away from the desired condition. The selected control strategy includes control parameters for the EMS, which, for example, may be determined from one or other model, or may use another value for the parameter that is at some other value such as a value intermediate those of the predictions of two models. Alternatively, if the uncertainty space indicates that a control parameter provided by both (or all) models is likely to lead to too inaccurate a prediction of the thermal condition whichever model was used, then the strategy may adopt a neutral or conservative value for that control parameter. The EMS is then operated in accordance with the selected strategy to control the environment in the building.
For example, consider a room with a skylight that has been missed from the original setup of the CSTM. Most of the time the CSTM produces a better result than the ANN and therefore tends to have a much higher or even dominant weighting in the control strategy selection. However as the year moves into summer and the sun starts to overheat the room with the skylight, the ANN starts to produce better results. The amount of data that supports the CSTM is much larger than the limited data that supports the ANN (for that room). However we now have a significant divergence in control strategies (based on each of the two models). As more data comes in, the HEMS assigns an increasingly higher probability of overheating to the CSTM. It can therefore take a more and more conservative strategy to heating that room on days that are forecast to be sunny.
At some point, possibly after several years, the HEMS will have enough data to create a separate subset of parameter space, in which the ANN becomes the dominant model and the CSTM is assigned a low probability of forecasting room temperature on forecast sunny days. In reality the HEMS will have more than one, possibly many, different parameters to take account of in this regard, not just the room temperature and the amount of sunshine forecast.
As a counter example, consider a room that has a tree starting to grow, so that it shades a window. Both models correct for solar gain through identifying that forecast sunny days have an impact on heating requirements. The first year that the tree shades the window the CSTM detects that the occlusion parameter in its solar gain model is wrong. The ANN will eventually catch up but requires more training data. During that period the CSTM has a much better track record but the heating input is considered risky (i.e. there are high levels of uncertainty) because of the divergence of the two model predictions. The HEMS therefore adopts the cautious approach of moderating to heat input based on the CSTM predictions.
These examples illustrate a number of features of the system:
It will be clear from the above that embodiments of the present invention have a number of advantages over the prior art.
It will also be appreciated by persons skilled in the art that various modifications may be made to the above embodiments without departing from the scope of the present invention as defined by the claims. For example, features from one embodiment may be mixed and matched with features from other embodiments.
| Number | Date | Country | Kind |
|---|---|---|---|
| 1503302.0 | Feb 2015 | GB | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/GB2016/050519 | 2/29/2016 | WO | 00 |
