This application claims priority to India Patent Application No. 3380/CHE/2012, filed Aug. 16, 2012, the disclosure of which is hereby incorporated by reference in its entirety.
The present disclosure relates in general to the field of power management, and more particularly, to a power management system for monitoring, controlling and reporting the consumption of power by plug-in devices, and the like.
Minimizing the power wastage has become a requirement towards sustainability. There can be a lot of wastage of power by a resource, in any entity. These resources can be primarily considered to be of two types: entity resources and miscellaneous resources. Entity resources would refer to resources utilized by an entity to conduct day to day activities, for example, desktop computers, laptops and copiers. Miscellaneous resources would refer to resources, for example, kitchen resources personal electronic devices and water coolers. For the purposes of this disclosure, these would be collectively referred to as ‘resources’. Each of these resources draws power and contributes to plug load. Power may be drawn when resources are in standby mode or not performing their primary function. The standby power use can be a significant contributor to plug loads. The term ‘plug load’, as used herein, refers to the power consumed by any resource that is plugged into a socket.
There exist separate systems for monitoring and controlling the high power loads of resources in a building using Building Management Systems (hereinafter referred to as ‘BMS’) for monitoring and controlling of high power loads in a building such as HVAC (heating, ventilation and air-conditioning) and smart plugs for controlling plug loads. A Building Management System (hereinafter referred to as ‘BMS’) is a system that can calculate the pre-set requirements of the building and control the building to meet the power requirements. Programs within these systems use captured information to decide the necessary level of control for resources within a building. The term ‘smart plugs’, as used herein, are typical plug strips which incorporate additional technologies to manage one or more resources. For example, smart plugs may incorporate technology to automatically disconnect power to certain resource when not in use. Smart plugs vary in design, but typically employ sensors, for example, occupancy sensors, load sensors, and timers.
The current resources do not have a reliable method to provide direct feedback for the power consumption by a resource. Typically a consumer has a periodic utility bill that allows for a comparison of the power costs from before and after the resource was installed. The cost of the consumed power shown in the utility bill does not take into account external factors, for example, temperature, rainfall, and hours of daylight, to allow a consumer to determine whether the usage of resource has actually resulted in a reduction in power consumption and/or an improved operational efficiency of the power consuming resources.
There exists a need to provide integrated solutions to extend BMS to plug loads so as to detect the power wastage, to adapt a power management policy implemented for an entity at the resource level. Most power management policies in an entity are time based and may not suffice to minimize power wastage based on recurrent events. The resource utilization information can be more effective by taking into account the real time information. Real time resource information can also be used to define effective power management policies. Further, resource utilization information correlated with power consumption is much needed.
The disclosure proposes an improved method and system for identifying power consumption patterns at each consumption point so as to curb the identified power wastage and make resources adaptable to power management policies.
Aspects of the disclosure relate to a system and method for identifying power consumption patterns at each consumption point so as to curb the identified power wastage and make resources adaptable to power management policies.
It is therefore one object of the present disclosure to provide systems and methods to identify the power consumption pattern at each power consumption point.
It is another object of the present disclosure to manage the power consumption by taking into account one or more external factors.
It is yet another object of the present disclosure to enable automatic enforcement of power management policies by conducting a pattern analysis of the resources.
The above as well as additional aspects and advantages of the disclosure will become apparent in the following detailed written description
The aspects of the disclosure will be better understood with the accompanying drawings.
While systems and methods are described herein by way of example and embodiments, those skilled in the art recognize that systems and methods disclosed herein are not limited to the embodiments or drawings described. It should be understood that the drawings and description are not intended to be limiting to the particular form disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the appended claims. Any headings used herein are for organizational purposes only and are not meant to limit the scope of the description or the claims. As used herein, the word “may” is used in a permissive sense (i.e., meaning having the potential to) rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to.
Disclosed embodiments provide computer-implemented methods, systems, and computer-readable media for identifying power consumption patterns at each consumption point so as to curb the identified power wastage and make resources adaptable to power management policies. The embodiments described herein are related to management of power consumption at the resource level. While the particular embodiments described herein may illustrate the invention in a particular domain, the broad principles behind these embodiments could be applied in other fields of endeavor. To facilitate a clear understanding of the present disclosure, illustrative examples are provided herein which describe certain aspects of the disclosure. However, it is to be appreciated that these illustrations are not meant to limit the scope of the disclosure, and are provided herein to illustrate certain concepts associated with the disclosure.
It is also to be understood that the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. Preferably, the present disclosure is implemented in software as a program tangibly embodied on a program storage device. The program may be uploaded to, and executed by, a machine comprising any suitable architecture.
The described system and method utilize deployable sensors that can be used to track and/or control at least one resource in conjunction with a variety of external factors.
The Event Management System 220 has sub-components which include a middleware 212, a plug load manager 214 and a graphical interface 216. Event Management System 220 is an application which monitors the individual plug load power consumption and develops patterns of power consumption at those points. The data is fed back from the server 120 to the event management system 220 where data is available through a graphical interface 216. The power management system 220 has a middleware 212. The middleware 212 includes at least one application programming interface (API) to provide various services for the application. Essentially, it maintains system integration, security, communications, scalability, cross-platform support etc. Actual functions and capabilities can vary between service providers. According to an embodiment of the present disclosure, middleware 212 comprises an incoming data handler 212a for processing data packets from gateway 118 and an outgoing data handler 212b for processing resource operations from graphical interface 216. The plug load manager 214 further comprises of a poll generator unit 214a, a pattern analysis unit 214b and an alert generation unit 214c. The pattern analysis unit 214b receives data from the historical data from server 120 and the sensor base station 122 readings to apply correlation techniques on actual values and historical values of resources, to predict future values of the resources 202. These future values can be used to enforce or make amendments to power management policies in an entity. According to an embodiment of the disclosure, the pattern analysis can be conducted at an individual resource level where each resource is considered independently for the analysis. Alternatively, the pattern analysis can be conducted by creating groups of resources based on their type or purpose. Pattern analysis can also be conducted at an entity level.
The power management system 220 can be pre-configured to assign weightages 304 to resources for the purposes of predicting utilization values. The weights are assigned based on influence of external factors 306 and the duration for which the estimation is being applied 308. For the purposes of illustration, if usage of resources like coffee machine and water heater can have an impact based on external weather, occupancy and unit pricing but the appliances such as printers, scanners, desktops will have effect only on occupancy and unit pricing but not on external weather conditions.
Various forecasting techniques may be employed 310 by the pattern analysis unit 214b to predict the power demand of resources 202. The sensor base station 122 collects enough historical data to build an appropriate model for forecasting for each sensor node. Preferably, models such as the Auto Regressive Integrated Moving Average (hereinafter may be referred to as ‘ARIMA’) may be utilized for power consumption information collection scheme. As data collected from sensor nodes arrive at the sensor base station 122, these can be collected and maintained for each sensor node. Based on the historical data, time series analysis methods can be applied to build up a data model, which can be used to forecast future sampling values. The prediction values of power consumption by a resource are based on the ARIMA model within a predefined tolerance value from their actual values. It incorporates three terms, namely, the Auto Regressive (AR) term, the Integrated term, and the Moving Average (MA) term and the general notation is ARIMA (p;d;q). The ‘AR term or ‘p’ is a linear regression of the current value of the series against one or more prior, known, values of the variable of interest. It captures the dependency of current value and its nearest prior values. The MA term or ‘q’ refers to the number of lags in the error term. The ‘Integrated’ term or ‘d’ indicates how many times one takes the difference of the dependent variable. It is the actual values rather than the forecasted values that are used as the lagged dependent dataset, and thus the historical dataset is updated with the latest actual value when the forecasting process moves forward. Sensor base station 122 keeps the latest ‘p’ states of the corresponding time series, where ‘p’ is the order of the AR term for that sensor node. The ‘p’ values are required for the prediction of next values. Once the sensor base station 122 receives the respective values and transfers to the power management system 220 through server 120 and middleware 212, the power management system starts the pattern analysis. The prior values would include historical values of the resources 202, for example, but not limited to, cost and utility bills. Using the historical data of the power consumption of resources, point estimates can be arrived at 312, by:
=((w1*Pn)+((w2)*Pn−1)+((w3)*Pn−2)+ . . . (wn−1)*Pn−(n−1)))/(w1+w2+ . . . +wn−1)
Where:
P=number of lag values
W=weightage assigned to a resource
If there is no influence of external factors, then the point estimate is assumed to be of a minimum value. If the influence of external factors varies based on the resource, then the energy management system 220 can be configured to adapt this value. These values, along with the resource information can then be made 314 available through a graphical interface 216.
The graphical interface 216 is the communication and control system that aggregates resource power consumption data for automated control based on a set of goals determined by at least one power management policy. Graphical interface 216 can provide relevant and timely information to an entity about the performance of at least one resource. The entity can comprise of several types of users, for example, occupants, administrators, technicians and executives. The graphical interface 216 can be used by occupants of an entity to enter their personal resources within their own control. For example, an occupant could choose to dim or turn off the task lamp and not use a coffee maker during this time, these preferences would be used by the command handler 118b in deciding which loads to switch off. The data available on the graphical interface 216 can be used to manage or enforce these power management policies 316. According to an embodiment of the present disclosure, a configuration section can be used to add resources and select curtailment priorities. A user can navigate through the configuration page to change resource priorities. The different group of resources can also have corresponding priorities. The resources 202 can be viewed on a priority based model, in which resources with low priority settings can be turned off or their power use is altered before ones with higher priority. The graphical interface 216 enables a user to go through its list of connected and controllable resources, exerting control when needed to meet a goal of power reduction. When an event occurs, a user can select the appropriate resources to turn off and send a command to the command handler 118b through the outgoing data handler 208b to cut power to the appropriate outlet. The term ‘event’ as used herein refers to a set of business rules applied for information processes pertaining to customer assistance, for the management of at least one resource. The events include the domain knowledge coded in the form of rules.
According to another embodiment of the present disclosure, resources that have the potential to shed the most loads and have no restrictions can be listed first as possible solutions. An operation page can be used to list details about the resource operation state and connection state. An events page can provide information about the events which have occurred in the past along with the current and future predicted events. The term operation state, as used, herein, means a resource's position that indicates weather a resource is on or not. The term connection state, as used herein, means a resource's position that indicates whether a resource is connected or disconnected from an electric outlet. The power management system 220 may be configured to select the default operation state of a resource as ON or OFF (0 or 1). Although some resources such as a printer may have features that enable them to turn off automatically or enter low power manually, these low power resources may need to be monitored for aggregate power consumption. User can utilize the priority configuration to select priorities in the order of which the resources may be shut down, to override control from the gateway 118, to toggle resources on and off remotely through the graphical interface, to view power consumption data of resources that are being metered, and to view a schedule of upcoming demand response events.
According to an embodiment of the disclosure, the plug load manager 214 comprises a poll generator unit 214a to redirect control commands received from the graphical interface 216 to any of the sub-systems of the power management system 220 through the outgoing data handler 212b and the command handler 118b. These control commands can used to implement the desired strategy for each resource. Alternatively, the control commands can be used to query at least one resource or a group of resources. The sensors can also be queried to generate data describing specific external conditions.
According to another embodiment of the disclosure, the plug load manager 214 comprises an alert generation unit 214c for generating and transmitting an automatic notification to a user upon the occurrence of an event. The alert generation unit 214c is adapted to transmit an alert message through several mediums, which include, but is not limited to, an electronic mail and phone text message. The graphical interface 216 can also generate alerts for a sensor data threshold breach for power management policy enforcement.
Having described and illustrated the principles of the disclosure with reference to described embodiments and accompanying drawings, it will be recognized by a person skilled in the art that the described embodiments may be modified in arrangement without departing from the principles described herein.
Number | Date | Country | Kind |
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3380/CHE/2012 | Aug 2012 | IN | national |