None
The invention is useful in energy management, and more particularly in the field of energy management in commercial buildings.
Energy use analysis in commercial buildings has been performed for many years by a number of software simulation tools which seek to predict the comfort levels of buildings while estimating the energy use. The underlying principles of these tools concentrate on thermal properties of individual elements of the building itself, such as wall panels, windows, etc. The complexity and level of detail required to accurately simulate a commercial building often makes its use prohibitive. The accuracy of such models has also been called into question in the research material. Following the construction and occupation of a new commercial building, the installed plant, such as boilers and air conditioning equipment, whose function is to provide suitable occupant comfort, is usually controlled by a building management system (BMS).
Through practical experience within the construction industry, it has become known that this plant is often over-sized and the use of the plant is often excessive. Common examples of this include plant operating for significantly longer than required including unoccupied weekends, heating and cooling simultaneously operating in the same areas due to construction or control strategy problems and issues with overheating and the use of cooling to compensate. Where the common problem of overheating occurs, the building envelope is quite efficient in dumping excess heat by radiation. In a similar manner, where buildings are over-cooled in summer, buildings are very effective in absorbing heat from the external environment to compensate. The utilization of this plant is not normally matched to the building envelope in which it operates and it is the intention to show how the method can help with this matching process.
U.S. Pat. No. 8,977,405 and publication US2015-0198961 A1 represent a series of methods developed to provide a high-level view of thermal performance in a commercial building. This view is quick to implement and easily understood by facilities and maintenance staff. The methods facilitate a better understanding of the thermal performance of a building envelope, as constructed, and the interaction between this envelope and the building's heating and cooling plant, as installed. The thermal performance of the building envelope and how it interacts with the plant has been expressed as a series of time lags and profiles which are functions of external temperature and solar activity. External temperature remains the most influential of the external weather parameters on energy usage. The lags and profiles have been developed to be derived from data which is readily available within modern conventional buildings.
Following U.S. Pat. No. 8,977,405, where the derivation of a building's natural thermal lag and the solar gain lag were presented, and publication US2015-0198961 A1 where a less data intensive method to calculate the natural thermal lag was presented, the following is an explanation of how the natural thermal lag can be used to derive a series of thermal profiles which can be combined to achieve automated optimization of thermal energy usage in commercial buildings during the heating season. While the absolute values of these lags, as they vary with external temperature, are important building thermal parameters in their own right, the profile of the relationship between these lag values and external temperature, as it varies over the full year's weather seasons, is more revealing about the building's thermal characteristics. In certain climates, the inclusion of solar activity in the lag relationship is required. This is for the simple reason that, depending on the building envelope, high solar activity during winter can affect the amount of heating required in a building, particularly in warm climates.
Two unique building thermal parameters have been defined. Unlike the building's natural thermal lag and solar gain lag described previously these parameters are derived from data while the building is being mechanically heated. They include mechanical heat-up rate and night-time natural cooldown profile slope.
The mechanical heat-up rate is a measure of how quickly the average space temperature in a suitable number of open spaces in a building reaches the desired heating set-point as measured from the space temperature at the time the heating system was started. This is measured in ° F./min and the mechanical heat-up rate will vary depending on the internal temperature observed when the heating systems switched on.
The night-time natural cooldown profile slope is a measure of how quickly the average space temperature in a suitable number of open spaces in a building naturally falls after mechanical heating has been switched off. It is the rate at which this cooldown happens naturally and has been shown to depend on the average daily lagged external temperature. The slope is measured from the time the mechanical heating stops to the time the mechanical heating starts up again (usually the following morning).
Both thermal parameters are dependent on the average daily lagged external temperature where the amount of lag applied has been determined by the building's natural thermal lag.
Both thermal parameters, which are unique to this commercial building, are used in combination with the weather forecast, particularly the forecast of external temperatures, to estimate the likely internal space temperature which will be present at the time the heating system will commence operation. The amount of time required to bring the internal space temperature to the desired set-point can also be estimated and with this information, it is possible to determine an optimum starting time for the heating system as a function of average daily lagged external temperature.
This invention provides a system and method to reduce the thermal energy used in a commercial building by use of thermal parameters which are derived from readily-available data both internal and external to the building.
The drawings listed are provided as an aid to understanding the invention
The invention provides a method performed by a system which connects directly to a commercial building management system (abbreviated as BMS).
This section describes the introduction of new thermal profiles, the manner in which these profiles along with the natural thermal lag described in U.S. Pat. No. 8,977,405 and publication US2015-0198961 A1 can be applied to the control of plant in a particular building, and finally, the application of these concepts to an actual building and the energy reduction results.
Following U.S. Pat. No. 8,977,405, where the derivation of a building's natural thermal lag was presented, and publication US2015-0198961 A1 where a less data intensive method to calculate the natural thermal lag was presented, the following is an explanation of how the natural thermal lag, along with a number of important thermal profiles, can be combined to achieve automated optimization of energy usage in commercial buildings. The following sections recap how the natural thermal lag is derived in U.S. Pat. No. 8,977,405 and publication US2015-0198961 A1, and also show the derivations of the mechanical heat-up rate and the night-time natural cooldown profile slope. Both of these have been shown to be closely correlated to the average daily lagged external temperature where the amount of lag used in calculating the average daily lagged external temperature is determined by the building's unique natural thermal lag.
The derivation of the building-unique natural thermal lag can be summarized as follows (from U.S. Pat. No. 8,977,405 and publication US2015-0198961 A1).
The natural thermal lag (NTL) of a commercial building is a unique property which indicates how quickly the internal spaces of the building respond to changes in external temperature. The NTL can be derived as follows:
If internal temperature is not available, apply the building energy to external temperature data regression analysis method as follows:
E
i=β0+β1(LTi)k=0.8+εi
where
The particular index of lagged average external temperature during the winter yields the low point of NTL sinusoid, while the particular index of lagged average external temperature during the summer yields the high point of the NTL sinusoid. This yields an approximated NTL plot over the full year (SHIEL003; publication US2015-0198961 A1).
NTLi=β0−β1Touti+εi
wherein
Once the particular relationship between NTL and daily average external temperature is established for said commercial building, the NTL can be estimated for any given average daily external temperature.
Plotting the individual values of the natural thermal lag derived from data for each day the building is at-rest is indicated in
This relationship can be statistically modelled as a simple linear regression of:
NTLi=β0−β1Touti+εi
The actual model derived for the test building B1 is:
NTL=12.93−0.555Tout±1.9
The parametric statistics which define this relationship are shown as an extract from the Minitab statistical analysis package:
The regression equation is
NTL=12.93+0.5546 Average Tout
This particular NTL response curve in
In SHIEL003, publication number US2015-0198961 A1, it has been shown how energy usage data of winter heating and summer cooling can be used to determine the optimum value of NTL for these seasons without any reference to internal temperature data.
In fact, these values of NTL for summer and winter represent the highest and lowest points of the sinusoid and therefore a method to determine the year-long NTL response for this building has been developed, based on energy usage and external temperature data alone.
This facilitates the simple estimation of the building's unique NTL to be used for energy efficiency purposes, in the event that rapid estimation is required or that a full year of internal space temperature data is unavailable.
The mechanical heat-up rate and the night-time cooldown profile slope are now defined. They are both useful in determining the best start times for heating plant based on the external temperature profile contained in a weather forecast. This section shows how these two thermal parameters can be applied to plant start times and are therefore used to reduce energy consumption in commercial buildings.
The mechanical heat-up rate (MHR) is a measure of how quickly the average space temperature in a suitable number of open spaces in a building reaches the desired heating set-point as measured from the space temperature at the time the heating system was started. See
The MHR will vary depending on the internal temperature observed when the heating systems switched on. The MHR is defined as the rate of increase of space temperature from that observed at heating system on time to the time at which the set-point is reached and can be described as:
MHRp=1 . . . N={(Tsetpoint−TSP
where Tsetpoint is the internal space temperature setpoint (usually 72° F.)
TSP
tsetpoint is the time required to heat the space from the starting temperature TSP
Each value of MHR is calculated for each day the heating system operates. Recording the average daily lagged external temperature for each of these days yields a series of MHRp=1 . . . N values for heating days 1 . . . N which can be plotted to show how the MHR varies with average daily lagged external temperature. It has been shown in practical use of this method that a linear regression relationship can be formed to show how the mechanical heat-up rate varies with average daily lagged external temperature. The amount of lag applied to determine the average daily lagged external temperature for this building is guided by the building's already determined natural thermal lag.
This relationship can be defined in general form as follows:
MHRi=β0−β1ALaggedTouti+εi
wherein
The night-time natural cooldown profile slope (NNCPS) is a measure of how quickly the average space temperature in a suitable number of open spaces in a building naturally falls after mechanical heating has been switched off. It is the rate at which this cooldown happens naturally and has been shown to depend on the average daily lagged external temperature. The slope is measured from the time the mechanical heating stops to the time the mechanical heating starts up again (usually the following morning).
The NNCPS is derived by first finding the relationship between the space temperature and the difference between this space temperature and the lagged external temperature over the period while the mechanical heating is switched off.
A regression model is derived to show how the internal space temperature changes as a function of the difference between that space temperature and the lagged external temperature for each heating day by using an equation:
T
SPi=β0−β1(TSPi−LaggedTouti)+εi
wherein
The slope of this linear relationship β1 is the NNCPS for this particular overnight period. By deriving several values of NNCPS, one for each day, and recording the average daily lagged external temperature during the same periods, a predictive relationship can be formed which indicates how the NNCPS will vary as a function of daily average lagged external temperature. This yields a series of NNCPSp=1 . . . N values for heating days 1 . . . N. This is shown in generalized form as follows:
NNCPSi=β0−β1ALaggedTouti+εi
wherein
The inventive method is described in
MHRp= . . . N={(Tsetpoint−TSP
MHRi=β0−β1ALaggedTouti+εi Eqn 2
Once the particular lagged external temperature is known, it is possible to forecast the approximate value of the MHR which will pertain to a commercial building based on a short-term weather forecast.
T
SPi=β0−β1(TSPi−LaggedTouti)+εi Eqn 3
This yields a series of NNCPSp=1 . . . N values for heating days 1 . . . N. A relationship can be established which links the NNCPS to the average daily average lagged external temperature and this is shown in generalized form in Eqn 4:
NNCPSi=β0−β1ALaggedTouti+εi Eqn 4
wherein
The method has been developed for practical implementation in real buildings. The majority of modern commercial buildings, be they office, retail, medical, educational, etc. are equipped with a building management system (BMS). The BMS is a computerized system which monitors vital parameters inside and outside the building and depending on the particular building-specific control strategy, the BMS will respond by switching plant on/off or if the plant has variable control, increasing/decreasing the level of output. Because of the need for high levels of reliability, availability and serviceability, most BMS are highly distributed in nature, meaning that one section of the BMS is completely independent of the others. This removes the risk of single points of failure in the overall system. The BMS hardware architecture therefore consists of control points (referred to as out-stations) which are autonomous but network connected. Each of these out-stations might monitor such things as several space temperatures and control multiple heating and cooling devices, in response to these monitored readings. The overall collection or framework of out-stations, monitors and controls go to make up the BMS. There are many manufacturers of these systems throughout the World; the largest might include companies such as Siemens (GR), Honeywell (US), Johnson Controls (US) or Trend (UK).
The most common form of communications within the BMS framework is a low level protocol called ModBus. This protocol was developed within the process control industry (chemical plants, oil refineries, etc.) and it dates from the earliest forms of computer control. The implementation concept of ModBus is that of addressable registers which are either readable, writable, or both. The easiest way to imagine the implementation is that of pigeon-holes. So with this protocol, it is possible to use a computer device, equipped with a ModBus hardware interface, to request the reading of a register (say register 8002) which might represent some space temperature (value can vary between 0000 and FFFF (in Hexadecimal) which, let's say, represents a temperature range of 0° F. to +200° F.). On reading this space temperature, the algorithm in the connected computer can now determine the response, so if the reading is 0x5EB8 (representing 74° F.), the computer might request that the heating valve be lowered and this is done by writing a new value to another register, say register 8006. The BMS will interpret this value and act accordingly. This assumes, of course, that the BMS is set up or programmed to monitor these registers and act accordingly. This protocol must be agreed with the BMS programmer in advance so that both sides of the ModBus registers are aware of the meaning and mapping of register addresses and values.
In the practical implementation of this system, the physical connection to the BMS is normally achieved over an industry-standard Internet Protocol (IP) network. This is the same type of network installed in a standard office or commercial building. Much development has been done by the BMS manufacturers in recent years to get the BMS protocols, such as ModBus, to function over a standard Ethernet or IP network. This has led to ModBus over IP. If a new computer is introduced to this Modbus over IP network, the new computer is simply assigned an IP address by the network administrator and thereafter, that computer can issue read and write commands over IP, once the map of registers is known to the new computer. As mentioned, this map is known to the BMS programmer, so the introduction of the new computer would preferably happen with the knowledge and agreement of the BMS programmer. The BMS programmer may assign certain rights and privileges to the new computer thus dictating what it can read and what it can control by register writes.
A typical configuration is shown in
401—Control outputs to chiller is typically a simple 0-5 v control signal to enable the operation. The signals also are used to enable the operation of cooling system primary and secondary pumps. If variable frequency drives are installed, this control group will also use a 0-10 v (or 4-20 mA) voltage (or current) controller to vary the speed of these pumps, depending on demand.
403—Status inputs from chiller is typically a Modbus connection which allows the chiller and variable frequency drives (if installed) inform the BMS of various operating parameters such as internal temperatures, speed of rotation, number of compressors in use at any time, etc. These inputs will also include status inputs from the pumps sent from a current transformer that will tell the BMS if the pumps are operating.
405—BMS outstation controlling cooling is a BMS out-station that contains the necessary control and monitoring devices to control the building's cooling system.
407—Control outputs to AHU are typically a simple run enable 0-5 v digital signal that turns the air-handling unit on or off and various 0-10 v analog valve controls to modulate the temperature of the supply airflow.
409—Status inputs from AHU will allow the air-handling unit to signal various important temperature and air flow parameters to the BMS.
411—BMS outstation controlling fresh air supply is a BMS out-station that contains the necessary control and monitoring devices to control the building's fresh air supply via air handling units.
413—Physical temperature sensor is the physical device typically wall or ceiling mounted which measures local temperature.
415—0-10 v input connected to 1st floor ceiling temperature sensor is the physical device within the BMS out-station to which the temperature sensor is wired. Readings of temperature can vary between zero and ten volts, the value of which represents a manufacturer's range of temperatures. The reference to 1st floor is purely by way of illustration. There will be several of these sensors in a commercial building.
417—1st floor space temperature register 8002 (read/only) is an illustration of an assigned register address within the Modbus register map which relates to this temperature sensor.
419—Modbus register read control is the module within the BMS, which ensures correct timing of read requests to the physical device to which it is connected.
421—Outstation control strategy logic and Modbus interface manager is the intelligence programmed into the BMS to tell it how to control the pieces of plant such as the heating or cooling systems. It also controls data access to and from the Modbus network.
423—Modbus register map contains the agreed assigned register addresses of each piece of physical hardware to which the BMS needs access over the Modbus network.
425—Heating boiler enable register 8008 (write/only) is an illustration of a write only register to which the correct data value can be written and which will result in the boiler being enabled with a digital ON/OFF signal.
427—Digital signal 0-5 v where 5 v represents boiler enable is the physical output from the BMS, which can be switched from zero to five volts to represent the switching on or enabling of the boiler.
429—Physical heating plant, which is expecting a digital signal to signify if it should turn on or off. The boiler will have further internal controls to ensure no overheating, etc.
431—Physical heating pump speed controller is an illustration of a physical variable frequency drive controlling a pump's speed or the pump itself being switched on or off by contactor. The BMS controls are capable of controlling either situation.
433—0-10 v output to the variable frequency heating pump control is the analog signal varying between zero and ten volts to signify the speed at which the heating pump should run.
435—Heating pump speed control register 8010 (write/only) is an illustration of an assigned Modbus address for the speed control of the heating pump.
437—Modbus register write control is the module within the BMS which ensures correct timing of write requests to the physical device to which it is connected.
439—Modbus over IP network is the Modbus transport and protocol layers which run over a standard Ethernet network.
451—Control outputs to chiller is typically a simple 0-5 v control signal to enable the operation. The signals also are used to enable the operation of cooling system primary and secondary pumps. If variable frequency drives are installed, this control group will also use a 0-10 v (or 4-20 mA) voltage (or current) controller to vary the speed of these pumps, depending on demand.
453—Status inputs from chiller is typically a Modbus connection which allows the chiller and variable frequency drives (if installed) inform the BMS of various operating parameters such as internal temperatures, speed of rotation, number of compressors in use at any time, etc. These inputs will also include status inputs from the pumps sent from a current transformer which will tell the BMS if the pumps are operating.
455—BMS outstation controlling cooling is a BMS out-station which contains the necessary control and monitoring devices to control the building's cooling system.
457—Control outputs to AHU are typically a simple run enable 0-5 v digital signal that turns the air-handling unit on or off and various 0-10 v analog valve controls to modulate the temperature of the supply airflow.
459—Status inputs from AHU will allow the air-handling unit to signal various important temperature and air flow parameters to the BMS.
461—BMS outstation controlling fresh air supply is a BMS out-station that contains the necessary control and monitoring devices to control the building's fresh air supply via air handling units.
463—Control outputs to heating system is a group of groups to enable the boilers and control heating pumps. These control signals are typically carried on physical 3 or 4-core shielded cables.
465—Status inputs from physical heating system and space temperature sensors is a group of inputs from components of the heating system such as pump running indicators, various heating water flow/return temperatures, building space temperatures, etc. These input signals are typically carried on physical 3 or 4-core shielded cables.
467 BMS Out-station controlling heating is the physical BMS outstation that carries out the control and monitoring of the building's heating system.
469 BMS live status monitor is a module that ensures that the connection to the BMS and Modbus network is physically and logically present.
471 Modbus interface manager ensures the correct flow of messages to and from the Modbus network.
473—BMS interface manager holds the agreed list of BMS specific commands, message structures and Modbus addresses to ensure correct mapping of Modbus registers to functional blocks within the BMS.
475—NTL, MHR and NNCPS calculation algorithms is a software module which takes monitored data and constantly updates the calculated building thermal parameters as described in this document for the more efficient control of the building heating plant.
477—Schedule files is a storage location for all plant schedules as determined by the continuous calculation of the thermal parameters based on recorded building data and the short-term weather forecast.
479 Temperature setpoints is a storage area for calculated setpoints as determined by the continuous calculation of the thermal parameters based on recorded building data and the short-term weather forecast.
481—Database is a local copy of the recorded building data such as space temperatures, etc.
483—Internet is the publically accessible IP network.
485—Weather forecast is a system which regularly retrieves a temperature and solar activity forecast for a location as close as possible to the building in question. This can also retrieve data from a building roof-mounted weather logging system.
487—Database is a large remote data storage area that holds a copy of all data held in the Inventive System locally within the building.
489—Status and reporting web service is a central facility for producing daily, weekly, monthly or annual reports of energy usage and building efficiency and producing alerts for unusual energy activity. These reports and alerts can be transmitted to the building owner/operator over the Internet.
491—Heating system optimizer in conjunction with 493 (Cloud-based replica of on-site system algorithms) contains the NTL. MHR and NNCPS algorithms unique to this building to facilitate the remote control of the energy management of this building if the local Inventive System suffers an outage due to technical difficulties.
The control strategy is agreed with the BMS programmer and the register mapping is shared between the BMS and the new computer device. This allows the new computer device to read and write certain registers. As an illustration, let's say, the computer device reads all internal space temperatures and the BMS external temperature. With this data, the computer device can calculate the natural thermal lag for the building over a one day period. With these space temperature data and knowledge of the start and stop times for the heating system, the computer device can calculate the mechanical heat up rate (MHR) and night-time natural cooldown profile slope (NNCPS) which according to the MHR and NNCPS algorithms explained in this document, can result in the computer device writing to the heating plant ON register to enable the boilers. In this way, the computer device can influence the heating control strategy by bringing forward or pushing back the mechanical heating start-up time.
Several interlocks can be implemented between the computer device and the BMS. These ensure that the BMS knows the computer device is functional. If, for any reason, the computer device fails to respond to the regular ‘are you alive’ request from the BMS, the BMS will revert to the stored control strategy and its default operational schedules. In this way, in the event of computer device or communications failure, no down time should be experienced by the BMS or the building.
Test Building Implementation of this Method
The method involving the various lags and profiles was implemented in a building in Western Europe for a 36 month period-referred to herein as year 1, 2 and 3, after a baseline year. This building has been referred to as the test building or B1. B1 is a single-tenant premium office building located at a city-center business park. Arranged as six floors over basement carpark, it comprises almost 11,000 m2 of usable office space (approximately 120,000 sq ft) and is concrete constructed with columns and cast in-situ flooring slabs. The building would be considered a heavy building unlike a more conventional steel-framed building and with that weight comes a larger thermal mass—slow to heat up and slow to cool down. All lag calculations were performed manually in preparation for their implementation in an automated computerized system.
Commencing with the establishment of an energy usage benchmark or baseline, the various lags and profiles were observed during the first month without any energy efficiency interventions. During this time, several open-office spaces were monitored and the internal and external temperatures were recorded. This data provided guidance for the initial assessment of how the lags might be successfully applied to the operation of the building plant. Note that the lags and lag profiles have been developed as (1) high level indicators of building envelope thermal performance and (2) indicators of how the building envelope interacts with the installed plant. In the B1 building, they have been used to guide reduced plant operations specifically to generate better energy efficiency in the use of plant to provide agreed levels of occupant comfort.
The following sections outline the baseline establishment, the specific actions taken as a result of the lag calculations and finally, the results of this implementation are described.
Before the energy reduction programme commenced, an energy usage baseline was agreed with the B1 building operator. After the operator had carefully considered the previous and following year's energy usage data and the weather experienced during these years, the figures from the full calendar year were selected as the most indicative of reasonable annual energy use.
Please note that all units used in the implementation of the method for the B1 building and reported here are S.I. or metric units as that what is now customarily used in Europe by building and design personnel. Where possible, the equivalent units from the US Customary system have also been included.
In order to show compliance with the national guidelines on occupant comfort temperature (from the UK and Ireland CIBSE Guide A), an environmental monitor was installed on March 24th, year 1. The monitor was located in an internal open plan office area on the first floor at the northern side of the building. There are two such open areas on each floor. Almost four week's data were logged on a 15 minute basis before any energy reduction intervention was implemented. The space temperature profile is shown in
On examination of the data, and with the Guide A guidelines in mind, certain observations can be made—
The space temperature measured on the 1st floor of B1 during this charted period is seldom within the recommended limits for the heating period of between 21° C. (70° F.) and 23° C. (73.4° F.). The space is considerably warmer and, as such, it could be assumed that the space is overheated, during the heating season of September to May.
In conjunction with space temperature, the Air-CO2 concentration was also monitored and this is shown in
The building is occupied from approximately 0730 to 1730 and this is reflected in the lowered parts per million (ppm) of CO2 outside of these hours. The following could be observed from the chart data:
The combination of observations shown for
Prior to April of year 1, the B1 building was operated on a full 24/7 basis with all plant enabled to run most of the time. This can be verified by the BMS plant schedules witnessed in February of year 1. The space temperature profiled in
In order to determine the building's actual operational hours, it was suggested to security staff that an informal log might be kept of approximate staff numbers using the building late at night and over the weekends. These observations, over a two month period, showed that the building was lightly used overnight and at weekends, varying between 10 and 25 people at any time at weekends.
Prior to April of year 1, the amount of thermal energy being driven into the building from the P1 boilers far exceeded the tabulated average values from the CIBSE design and operation guidelines. According to CIBSE Guide A, thermal energy input to an office building should be in the vicinity of 210 kWh/m2/yr for typical usage and 114 kWh/m2/yr for good practice usage. B1 was consuming 347 kWh/m2/yr during the course of the baseline year, based on a usable office space figure of 9,350 m2 (approximately 100,000 sqft).
Likewise, electricity usage numbers were 350 kWh/m2/yr in the baseline year, while the CIBSE usage guideline for typical office buildings was 358 kWhr/m2/yr and 234 kWh/m2/yr for good practice office buildings. The energy usage figures from CIBSE for typical office, good practice office and actual baseline year are shown in
Once the overheating issue was identified, the amount of chilling going into B1 also came under scrutiny. It was suspected that the over-heating of the building had a direct effect on the amount of chilling demanded by the individual fan coil units (FCU) on all floors. The BMS schedules for heating and chilling were first examined in February of year 1 and found to be running close to 24 hours per day.
It was reasonable to assume that the chiller schedule, starting at 2 am, was set up to avoid overheating during the early morning hours. If overheating could be reduced, the amount of chilling required might also be reduced.
The air handling units (AHU) were scheduled to run on a 24/7 basis. Given the B1 boilers were similarly scheduled, this meant the building was being supplied with tempered air at all times. Again an energy reduction opportunity presented itself based on the recommended fresh air flow in CIBSE Guide A at between 6 and 15 l/s/person (litres/sec/person), depending on the design parameters. This is almost identical to recommendations in ASHRAE Standard 55 for buildings in the USA. The four AHUs in B1, operating at full power, can deliver 28,000 l/s into the building. Significant losses in airflow are inevitable in the long non-linear ducts between AHU and office vents, but from the ventilation design, the fresh air supply is well in excess than that required for the current 500 occupants. The designers would have sized the AHUs for a maximum number of occupants, particularly in meeting rooms and open areas, such as the restaurant. With a reduced staff count at weekends, a reduced airflow is also possible. With the AHUs installed in B1, there was no mechanism to reduce the fan speeds—they are either on or off.
Monitoring of CO2 levels in open plan offices areas (shown in
Changing B1 BMS from Demand Driven to Schedule Driven Operation
When first analysed, the BMS was found to have been programmed as a demand-driven system. The underlying assumption is that heating and cooling were available from the main plant at all times and one relies on the correct functionality of the local FCUs to use the heat and cooling resources as required.
One of the potential drawbacks of demand driven systems can manifest itself if FCUs are left permanently on or are malfunctioning. There is a possibility that a heating and/or cooling load could always exist, whether the space is in use or not. In any case, the fact that the boiler or chiller is enabled overnight will create a load just to keep these systems available in standby.
It was recognised during April of year 2, that substantially better control could be achieved if the BMS was changed from demand driven to time schedule driven. This would allow observation and confirmation of occupant comfort temperature compliance given various small and incremental changes to the delivered environment. In changing to a time schedule control strategy, a much finer level of control would be available and it would be possible to lower the amount of the heat delivered to P1 in a controlled manner. It was hoped the amount of chilling required by P1 could also decrease with the smaller amount of delivered heat. The calculation of the various lags and profiles were facilitated by this change from a demand to a schedule driven BMS strategy. The changes to plant operations suggested by these lags and profiles could also be more easily implemented with a schedule driven system.
Following data collection from existing sources such as the BMS, newly installed monitoring equipment and observation, the following models were derived from this data. Data mainly comprised local external temperature and global radiation (sunshine), internal space temperatures and CO2 levels (various) and energy usage by plant type (boiler). These data proved sufficient to complete the profile model calculations as indicated in
The practical application of the invention taught herein to the B1 building forms part of an overall energy efficiency program. Many measures were implemented simultaneously or following each other over a comparatively short timescale, This was done as it would prove commercially impossible to separate out all of the individual measures and accurately report on the reduction effects of each one. For this reason, the figures showing the energy usage reduction in the following sections are for the complete program, rather than just the implementation of the material contained in this specification. However, the use of the mechanical heat-up rate and the night-time natural cooldown profile slope both contributed to the dramatic changes in energy efficiency in the heating of the B1 building.
The following sections are intended to show the gradual changes made to the BMS plant schedules. This occurred over an 18 month period. The pace of the BMS schedule updates ensured no sudden or noticeable environmental changes in B1.
The energy reduction programme has primarily focussed on the large plant and equipment. The first interventions concern the heating, chiller and ventilation schedules.
It is evident from the schedules in
A number of the listed interventions are operational in their nature while others, such as those on 9/6/year 1 and 7/10/year 1, are attempts at solving building equipment issues which were affecting energy reduction efforts.
Note the change that occurred on February 25 of year 2 when the BMS was upgraded to a more recent version. This enabled the full control of the recently installed Variable Frequency Drives (VFDs) on the AHU fan motors. It also allowed for logging of certain important data points in B1 on a 15-minute basis. A VFD is an electrical device that is capable of running a large electric motor at a variable speed. They are in common usage in the HVAC industry and are capable of running both fan and water pump motors. Given the occupancy patterns in B1, particularly at weekends, it was recommended that the four AHUs be equipped with these devices in an effort to further reduce energy consumption.
A number of important changes in BMS schedules and set-points resulted in reductions in energy use in B1 which will be enumerated in this section. The analysis of heating and chilling patterns guided by the mechanical heat and cooling lags and the equivalent natural cooling lags, were also instrumental in identifying the inefficiencies which caused B1 to be over-supplied with both heat and chilling.
The energy usage in B1 had been divided into fixed and variable energy sinks. To re-cap, heating in summer is confined to Domestic Hot Water or DHW and cooling during the winter months is limited to serving locations of B1 which over-react to winter heating. For this reason, the use of the air-chiller has been shown to be relatively constant over the winter months just as the heating load or DHW is relatively constant in summer. This effectively divides energy use in B1 into landlord and tenant usage. Landlord usage is a common concept in commercial buildings where the landlord is responsible for supplying heating, cooling and ventilation and these services are often separately metered. The tenant part is that which is used on each floor such as small loads due to local power and lighting. It is the part of the overall energy bill normally paid for directly by the tenant.
The chart in
The lowering trend in heat energy consumed in B1 is apparent from this graph. Once control was gained over the level of heat being introduced to the building, the usage was observed to fall. Several of the early interventions during April and May of year 1 contributed to this decrease.
The cooling delivered to B1 over the same period of January of the baseline year to December of year 3 is shown in
From the graph in
This compares the energy consumption on a monthly basis over the course of the baseline year with the equivalent month in year 3.
The energy usage pattern continues to show year-on-year improvement equating to reductions of 34% in year 1, 53% in year 2 and 54% in year 3. This is consistent with the energy reduction process as described in SHIEL002, U.S. Pat. No. 8,977,405. The continuous iterations to find out-of-control or poorly controlled plant continues and the improvements are evident but naturally slowing down considerably.
The air quality and temperature experienced in the same open plan area of B1, measured during March of year 1, prior to any energy efficiency interventions was constantly monitored during the three year process. The temperature profile from March of year 3 is shown in
The Air-CO2 concentration levels have become slightly higher based on the observed data plotted in
Closing remarks. The savings achieved in B1 represent an overall saving of 54% based on a direct comparison of year 3 versus the baseline year total energy consumption figures. It is clear that B1, as with many other buildings that have been examined, that substantial overheating was the norm. This in turn, caused substantial over-cooling to compensate. Both heating and cooling are expensive services in any western country and they should be limited to what is required for the building to provide a good working environment to occupant. When considering the quality of the thermal environment of any commercial building, there is nothing to be gained from overheating or overcooling.
Building plant has been sized to cater for the worst weather conditions and the maximum number of occupants. Whether these maximum conditions are ever met, is unclear, but equipment such as chillers, air handling units and boilers are very large consumers of power and gas and as such, they need to be controllable, rather than simply turned on and off.
The system and method described in this document, along with the lags described in SHIEL002—U.S. Pat. No. 8,977,405—and SHIEL003—publication number US2015-0198961 A1—were applied to this building. This application resulted in substantial improvement and reduction of energy usage, while preserving the delivery occupant comfort, and in certain respects, such as air quality, improving it.
This application is a continuation in part of U.S. application Ser. No. 14/606,989 by the same inventor, entitled Method for determining the unique natural thermal lag of a building, filed Jan. 27, 2015, docket SHIEL003, publication number US2015-0198961 A1. The entirety of application Ser. No. 14/607,003 is incorporated by reference as if fully set forth herein. This application is also related to U.S. application Ser. No. 13/906,822, entitled Continuous Optimization Energy Reduction Process in Commercial Buildings, filed May 31, 2013, docket SHIEL002, now U.S. Pat. No. 8,977,405, the entirety of which is incorporated by reference as if fully set forth herein. This application is also related to U.S. application Ser. No. 14/607,011, entitled Building Energy Usage Reduction by Automation of Optimized Plant Operation Times and Sub-Hourly Building Energy Forecasting to Determine Plant Faults, filed Jan. 27, 2015, docket SHIEL005, and where the entireties of SHIEL005 is incorporated by reference as if fully set forth herein.
Number | Date | Country | |
---|---|---|---|
Parent | 14606989 | Jan 2015 | US |
Child | 14966300 | US | |
Parent | 13906822 | May 2013 | US |
Child | 14606989 | US | |
Parent | 13374128 | Dec 2011 | US |
Child | 13906822 | US |