Energy control and predictive model for building automation system

Abstract

Methods and systems are provided for controlling an electrical load of an electrical power source from a load facility. The method comprises: determining a set of electrical loads; determining a set of conditions; predicting energy consumption using a machine learning model based on the set of electrical loads and the set of conditions; visualizing the energy consumption on a display device; and controlling the set of electrical loads under the set of conditions.

Description

Technical Field

[0001] The present disclosure generally relates to energy control for building automation systems. More specifically, the present disclosure relates to systems and methods for energy control and predictive modeling for building automation systems, including, for example, using historical data and / or machine learning to determine engaging events and reducing the overall energy intensity of a facility by predicting the outcomes of proposed reduction actions. Background Art

[0002] Building automation systems are used to coordinate, manage, and automatically control various environmental, physical, and electrical building subsystems, including heating, ventilation, air conditioning, and refrigeration (HVACR) systems and climate control that may also include security, lighting, power, and the like. HVACR systems may include, for example, rooftop units, etc., to provide conditioned air to an air distribution system, such as a duct system and / or any other suitable equipment and / or subsystem. In an HVACR system, conditioned air is delivered to a building or occupied space. An air handler or the air handler portion of an air conditioning unit used to meet the HVACR needs of a building typically includes a heat transfer circuit system housed within a sheet metal housing. The heat transfer circuit may include one or more compressors, condensers, evaporators, fans, filters, dampers, and various other devices. The compressor, condenser, expansion device, and evaporator are fluidly connected. Summary of the invention

[0003] Features of the embodiments disclosed herein relate to demand response, virtual power plants, and / or other applications designed to reduce the energy intensity of facilities such as buildings, campuses, factories, etc. It should be understood that although an air distribution system is provided as one of the examples, the features of the embodiments disclosed herein can be applicable to a wide variety of HVACR equipment, systems, and / or subsystems. Features of the embodiments disclosed herein can allow users (e.g., operators of facilities, etc.) to participate in events (e.g., demand response events, energy reduction events, energy consumption events, etc.) as planned, and / or define rules or conditions for applying one or more strategies or scenarios (e.g., reduction strategies such as energy reduction strategies, carbon dioxide reduction strategies, etc.). Features of the embodiments disclosed herein can consider historical data (e.g., trend data of the electrical load of the facility, etc.) and / or use machine learning models to predict results (e.g., energy consumption, carbon dioxide levels or effects, temperature, relative humidity, manufacturing capacity, etc.) and the effectiveness of available strategies or scenarios to select the best strategy or scenario. It should be understood that the predicted results can include financial savings, risks to the comfort of occupants of the space in the facility, degradation of the manufacturing process, etc.

[0004] Features of the embodiments disclosed herein may reduce the overall energy intensity of a facility, thereby reducing costs associated with energy consumption while maximizing occupant comfort and / or manufacturing output associated with the cost savings.

[0005] Features of the embodiments disclosed herein may include scheduled participation in events such as demand response events, scheduled application of energy reduction (or consumption) actions, and / or rule-based participation in both demand response events and energy reduction (or consumption) actions.

[0006] Features of the embodiments disclosed herein may also include: using historical data (e.g., trend data of a facility's electrical load, etc.) and / or machine learning models to predict the effectiveness of various strategies and / or scenarios, allowing a user to select from available resources (e.g., electrical load, etc.) to compare "what-if" scenarios, providing visualizations that allow users to compare various scenarios, and allowing the disclosed building automation system algorithms to automatically select from available resources to maximize benefits (e.g., reduced energy consumption, reduced carbon dioxide levels or impacts, etc.) and minimize risks to occupants and / or manufacturing processes.

[0007] Features of the embodiments disclosed herein may also include: algorithms that learn and / or continue to learn to make improved predictions and automated decisions, allowing a user to define acceptable conditions for each resource and combinations thereof, and / or providing the user with the option of defining a precedence (e.g., priority, etc.) for applying each resource to the overall reduction (or consumption) strategy.

[0008] A building automation system for controlling electrical loads from load facilities of a power grid using demand response is provided. The system includes a controller and a memory configured to store a plurality of predetermined conditions. The controller is configured to: receive a request for a demand response event, determine a plurality of current conditions, automatically participate in or exit the demand response event based on the current conditions and the predetermined conditions, and notify an operator responsible for the demand response event at least when exiting the demand response event.

[0009] A method for controlling electric loads from a load facility of an electric power source is provided. The method includes: determining a set of electric loads; determining a set of conditions; predicting energy consumption using a machine learning model based on the set of electric loads and the set of conditions; visualizing the energy consumption on a display device; and controlling the set of electric loads under the set of conditions.

[0010] A building automation system for controlling electric loads from a load facility of an electric power source is provided. The system includes a controller and a memory configured to store a machine learning model. The controller is configured to: determine a set of electric loads; determine a set of conditions; predict energy consumption using the machine learning model based on the set of electric loads and the set of conditions; visualize the energy consumption on a display device; and control the set of electric loads under the set of conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings illustrate various embodiments of systems, methods, and embodiments of various other aspects of the present disclosure. It will be appreciated by those skilled in the art that the element boundaries (e.g., boxes, box groups, or other shapes) shown in the accompanying drawings represent an example of boundaries. In some examples, an element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of an element may be implemented as an external component in another element, and vice versa. A non-restrictive and non-exhaustive description is described with reference to the following drawings. The components in the accompanying drawings are not necessarily drawn to scale, but are focused on the explanation principle. In the following specific embodiments, the embodiments are described only as explanations, because various changes and modifications may become apparent to those skilled in the art according to the following specific embodiments.

[0012] Figure 1 A schematic diagram of a building automation system according to an embodiment is shown.

[0013] Figure 2 A schematic diagram of a building automation system controller is shown according to an embodiment.

[0014] Figure 3 A flow chart of a method for controlling electrical loads from load facilities on a power grid using demand response is shown according to an embodiment.

[0015] Figure 4 A flow chart of a method for controlling an electric load from a load facility of an electric power source according to an embodiment is shown.

[0016] Figure 5 A user interface sequence of a method for controlling an electrical load from a load facility of an electrical power source according to another embodiment is shown.

[0017] Like reference numerals refer to like parts throughout. DETAILED DESCRIPTION

[0018] In the following detailed description, reference is made to the accompanying drawings, which form a part of this specification. In the drawings, similar symbols generally identify similar components unless the context specifies otherwise. In addition, unless otherwise specified, the description of each consecutive figure may refer to features from one or more previous figures to provide a clearer context and a more substantial description of the current example embodiment. Likewise, the example embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments may be utilized, and other changes may be made without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that aspects of the present disclosure, as generally described herein and shown in the drawings, may be arranged, replaced, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein.

[0019] It should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be embodied in various forms. Well-known functions or structures are not described in detail to avoid unnecessary details that obscure the present disclosure. Therefore, any specific structural and functional details disclosed herein should not be interpreted as limiting, but only as a basis for the claims, and as a representative basis for teaching those skilled in the art to utilize the present disclosure differently with substantially any appropriate detailed structure.

[0020] In addition, in this article, the present disclosure may be described in terms of functional block components and various processing steps. It should be understood that such functional blocks may be implemented by any number of hardware and / or software components configured to perform the specified functions.

[0021] The scope of the present disclosure should be determined by the appended claims and their legal equivalents, rather than by the examples given herein. For example, the steps recited in any method claim may be performed in any order and are not limited to the order presented in the claim. In addition, no element is essential to the practice of the present disclosure unless specifically described as "critical" or "essential" herein.

[0022] As defined herein, the term "building automation system" or "BAS" may refer to systems used to coordinate, manage, and automatically control various environmental, physical, and electrical building subsystems (e.g., HVACR systems and climate control that may also include security, lighting, power, etc.).

[0023] As defined herein, the term "demand response" may refer to changes in electricity consumption by, for example, electric utility customers to better match electricity demand and supply. It will be appreciated that various embodiments of demand response are described in US Pat. No. 11,677,239, which is incorporated herein by reference in its entirety.

[0024] As defined herein, the term “virtual power plant” or “VPP” may refer to a power plant, such as a distributed power plant, that aggregates the capacity of distributed energy resources for purposes such as enhancing power generation, trading or selling power in electricity markets, and / or for demand-side options for load reduction.

[0025] As defined herein, the term "load shedding" may refer to an action for reducing the load on a power source (e.g., a power grid, a power plant such as a virtual power plant, etc.), such as interrupting the power supply to avoid excessive load on the power plant. It should be understood that when the demand for electricity is greater than the power source supply capacity, load shedding can be used to relieve the pressure on the power source. It should also be understood that a load facility may refer to a facility (e.g., a building, a factory, a park, etc.) including multiple resources with corresponding amounts of electricity, which can be cut by multiple resources during an event such as a demand response event, for example. For example, when a request for a demand response event or an energy reduction (or consumption) action is assigned to a load facility participating in a demand response or energy reduction (or consumption) plan (e.g., from a power source such as a power grid, a VPP, etc.), unless a user (e.g., an operator of a load facility, etc.) exits the demand response or energy reduction (or consumption) event, the load facility may respond (e.g., by load shedding (such as shutting down and / or reducing the power supplied to one or more electrical loads, etc.)).

[0026] Figure 1 A schematic diagram of a building automation system (BAS) 100 according to an embodiment is shown. It should be understood that in addition to traditional capabilities, the BAS may also require additional and / or specific sensors in addition to sensors typically provided for equipment and / or building control. The BAS may also include machine learning algorithms, predictive models such as machine learning models, etc., which can collect historical information (including additional and / or specific sensors and data measured or sensed by these sensors) to predict scenarios and outcomes. The BAS may also include cloud connectivity and / or provide remote access to the BAS to expand the capabilities of the features in the embodiments disclosed herein.

[0027] In an embodiment, the BAS 100 includes a BAS controller 200 that is communicatively coupled to a plurality of building automation networks (BANs) 110, 120, 130. The BANs 110, 120, 130 may facilitate communication between the BAS controller 200 and any number of BAS terminal devices 111, 112, 121, and 131. The terminal devices 111, 112, 121, and 131 may include, for example, a range of HVAC equipment such as chiller units, chillers, air handlers, furnaces or boilers, variable air volume boxes and dampers, temperature or humidity sensors for monitoring spaces, and the like with a plurality of data sensors that produce a continuous data stream. In an embodiment, the terminal devices include panels, sensors, controllers, microprocessor-controlled devices, converters, thermostats, furnaces, heating systems, chillers, cooling systems, air conditioners, air filters, air purifiers, fire and life safety systems, security systems, alarm systems, occupancy sensors, electrical system monitors and controllers, lighting system monitors and controllers, ventilation system monitors and controllers, smoke sensors, light sensors, motion sensors, humidity sensors, pumps, air handlers, fluid and air moving and handling equipment, terminal equipment, life science and pharmacology control equipment and monitoring systems, positive pressure clean rooms, negative pressure clean rooms, industrial automation and control equipment and systems, programmable logic controllers, etc.

[0028] The BAS controller 200 includes a communication interface 201 that couples the BAS controller 200 to the BAN 110. The BAN 110 includes a plurality of terminal devices 111 and terminal devices 112 that are coupled to the BAN 110 using, for example, a media converter 113. The terminal devices 111, 112, 121 may be, for example, chillers, air handlers, furnaces, boilers, variable air volume boxes, dampers, temperature or humidity sensors, etc. The BAS controller 200 also includes a communication interface 202 that couples the BAS controller 200 to the BAN 120. The BAS controller 200 also includes a communication interface 203 that couples the BAS controller 200 to the BAN 130. The BAN 130 includes terminal devices 131, such as temperature sensors, humidity sensors, carbon dioxide sensors, etc. The BAS controller 200 also includes a data network interface 204 for transmitting data to one or more user devices 141 via a data network 140 (e.g., a private LAN and / or the public Internet). The user device 141 may include a mobile phone, a smart phone, a tablet device, a smart watch, a pager, a notebook computer, a desktop computer, etc. The remote server 160 is communicatively coupled to the BAS controller 200 via the data network 140 and stores information for a plurality of terminal devices, including information related to a particular product, product version, firmware version, and / or software version.

[0029] Figure 2A schematic diagram of a building automation system controller 200 according to an embodiment is shown. The BAS controller 200 includes a processor 210 in operative communication with a memory 220, a user interface 230, and a data storage device 240. The memory 220 includes a random access memory, which can be used, for example, to store temporary data, calculation results and intermediate results, input / output buffering, graphical user interface buffering, program execution, and / or any other suitable purpose. The data storage device 240 includes a non-volatile memory that can be used to store data that persists across power cycles, such as a flash drive, an electrically erasable programmable read-only memory, a read-only memory, a magnetic hard disk, a solid-state disk (SSD), a hybrid drive (combined hard disk / SSD).

[0030] The user interface 230 includes multiple elements for facilitating user input and displaying output to the user, and may include one or more of buttons, switches, light-emitting diode indicators, character displays (such as liquid crystal displays or vacuum fluorescent displays), graphic displays (such as thin film transistor displays), touch screen displays, etc.

[0031] Figure 3 A flow chart of a method 300 for controlling electrical loads from load facilities on an electrical grid using demand response is shown, according to an embodiment.

[0032] It should be understood that, unless otherwise specified, the process flow 300 disclosed herein may be executed by one or more controllers or processors (including, for example, Figure 2 200 and / or processor 210, any other suitable processor, and / or combination thereof).

[0033] It should also be understood that the processing flow 300 may include one or more operations, actions, or functions illustrated by one or more of the blocks 310, 320, 330, and 340. These various operations, functions, or actions may correspond, for example, to software, program code, or program instructions executable by a processor that causes these functions to be performed. Although illustrated as discrete blocks, significant modifications may be made depending on the desired implementation, for example, two or more blocks may be reordered; additional blocks may be added; and various blocks may be divided into additional blocks, combined into fewer blocks, or removed. It should be understood that operations including initialization and the like may also be performed prior to the processing flow 300. For example, system parameters and / or application parameters may be initialized. The processing flow 300 may begin at block 310.

[0034] At block 310 (Receive Event Request), a controller (e.g., a controller of a building automation system in a load facility, etc.) may receive a request for an event from a power source (e.g., a power grid, a power plant such as a virtual power plant, etc.). In an embodiment, the event may be a demand response event dispatched from the power grid, an energy reduction (or consumption) event from a power plant, etc. The event may include an amount of power to be reduced (or consumed), a duration (e.g., a period of time) of power reduction or consumption, etc. The load facility may include multiple resources (e.g., electric loads, etc.) having corresponding amounts of power that can be reduced (or consumed) by the multiple resources during the event.

[0035] In an embodiment, when a request for an event is dispatched to a load facility participating in a demand response or energy reduction (or consumption) program, the load facility (e.g., a controller of the load facility) may respond to the event unless the operator of the load facility opts out of the called or requested event. In some demand response or energy reduction (or consumption) programs, the operator of the load facility may be allocated a certain number of opt-out opportunities within a period of time (e.g., one year, etc.) before the operator of the load facility begins to lose incentives to participate in the program.

[0036] In an embodiment, the resources of the load facility (e.g., electrical loads, etc.) can be allocated corresponding amounts of electricity that can be cut (to reduce demand) or consumed (to increase demand) by these resources during the event. In the event of reducing the demand on the power grid or reducing the energy consumption of the power plant, the controller can, for example, temporarily reduce or interrupt the power consumption of the resource, transfer the power consumption of the resource to another time period, and / or use on-site power generation or power storage to replace the power grid or power plant. In the event of increasing the demand on the power grid or increasing the energy consumption of the power plant, the controller can, for example, temporarily increase the power consumption of the resource, transfer the power consumption of the resource from another time period, enable or turn on additional resources, change the efficiency of the resource to consume more electricity, and / or operate the resource to generate energy (e.g., electrical energy, thermal energy) for on-site storage. Processing can proceed from box 310 to box 320.

[0037] At block 320 (Apply Predefined Rules), the controller may apply a plurality of predetermined rules or conditions to determine whether to participate in or exit the event. It should be appreciated that features of the embodiments disclosed herein provide an operator with the ability to define (e.g., predetermine, predefine, etc.) a set of rules or conditions that, when applied (e.g., compared to current conditions, etc.), may result in a decision, such as participation in or exit from a demand response or energy reduction (or consumption) program. In an embodiment, the predetermined rules or conditions may be stored, for example, in a memory.

[0038] In an embodiment, the controller may obtain or determine a set of current conditions (e.g., measurements, configurations, etc.) via, for example, sensors, devices, electrical loads, etc. In an embodiment, the controller applying a predetermined rule or condition may include comparing the current condition to the predetermined rule or condition. For example, an operator may define a set of rules including: (1) if the current condition (e.g., an environmental condition, such as ambient temperature, ambient relative humidity, ambient pressure, ambient carbon dioxide level, etc.) satisfies the predetermined rule or condition (e.g., exceeds a user-defined threshold in one embodiment, or is equal to or less than a threshold in another embodiment), (2) there is sufficient electrical load available for reduction in the load facility (e.g., the power demand of the available electrical load is greater than the power demand requested for the event), and / or (3) the event does not cause significant disruption to personnel and / or processes at the facility (e.g., after load reduction for the event, the electrical loads operating at full or partial capacity can provide sufficient or maximized comfort to personnel, such as to maintain sufficient temperature, relative humidity, carbon dioxide level, pressure, etc., and / or provide sufficient power to electrical loads used for manufacturing processes to maintain or maximize manufacturing output, etc.), then the event may be automatically attended without operator intervention. If the current conditions do not satisfy a predetermined set of rules or conditions, the event may be automatically exited without operator intervention. Processing may proceed from block 320 to block 330.

[0039] At block 330 (Automatically Participate or Opt Out), the controller may automatically determine to participate or opt out of the event based on applying or satisfying a plurality of predetermined rules or conditions at block 320 .

[0040] In an embodiment, the predetermined set of rules or conditions may include a single rule or condition, a combination of rules or conditions, and / or customized rules or conditions that provide operators with flexibility in controlling the resources of the facility to proactively determine participation or exit from an event. It should be understood that applying the predetermined rules or conditions may include one or more parts, equations, and / or comparisons. For example, applying the predetermined rules may include comparing the number of people (current conditions) in all or part of the facility with a threshold value (predetermined rule or condition), the ambient temperature (current conditions) with a threshold value, the humidity level or relative humidity level (current conditions) with a threshold value, the level of sunlight (current conditions) with a threshold value, a measurement of manufacturing capacity (current conditions) with a threshold value, a measurement of machine load (current conditions) with a threshold value, a measurement of acceptable comfort for building occupants (current conditions) with a threshold value, the amount of time since the load was shed in the event (current conditions) with a threshold value, the amount of time elapsed since the last load shedding event (current conditions) with a threshold value, etc. If the current condition exceeds the corresponding predetermined threshold (predetermined rule or condition) in one embodiment, or is equal to or less than the threshold in another embodiment, the controller can decide to automatically participate in the event without operator intervention; otherwise, the controller can decide to automatically exit the event without operator intervention.

[0041] In an embodiment, applying a predetermined set of rules or conditions may include: when, for example, the controller controls the resource to perform load shedding, satisfying the operator's preference (or priority) for the order in which the resources are to be cut (e.g., from highest priority to lowest priority, from lowest priority to highest priority, etc.). If the predetermined set of rules or conditions are applied or satisfied, the controller may decide to automatically participate in (or exit) the event without operator intervention. For example, the controller may determine a set of electrical loads (i.e., which electrical loads may be selected for, for example, load shedding, etc.) based on the power requirements of the event (e.g., how much power to cut, etc.), the power requirements of each electrical load (e.g., how much power is required to run the electrical load, etc.), and / or the priority of each electrical load (e.g., to select or cut the electrical load with the lowest priority first or last, etc.).

[0042] In an embodiment, the controller may evaluate the current conditions of the facility, the calendar, and / or any other applicable measurements based on a predetermined set of rules or conditions to determine participation or withdrawal from the event.

[0043] In an embodiment, a predetermined set of rules or conditions may include a predetermined schedule. The operator and / or controller may arrange to participate in or exit the event before the actual notification or request. For example, in the months of, for example, May to August (including May and August or excluding May and August), it may be known in advance that for a particular facility, the manufacturing output on each Friday of the month may be much less than usual. At the same facility, the number of office staff may be much less than usual due to the hybrid work options provided to employees. In such an embodiment, the operator and / or controller can arrange to participate in all summer / Friday events in advance because such participation causes minimal interference to the business of such a facility. In another example, the operator and / or controller may know in advance that participating in an event may cause great damage to the business of the facility, in which case the operator and / or controller may preferably automatically exit before the notification or request of the event. For example, in some businesses, the last week of each month may be very busy because people and machines work at full capacity to complete the monthly quota, so participating in an event during such peak business operations may not be preferred.

[0044] It should be understood that the predetermined schedule (e.g., a specific day of a week in a specific month, a specific week of each month, etc.) can be stored in the memory. When the current schedule (current day, current week, current time period, etc.) meets the predetermined schedule (e.g., falls within a predetermined date and / or time), the controller can automatically participate in or exit the event.

[0045] It should be understood that each time the controller automatically exits the event, an event counter (e.g., initialized to zero during system initialization and cleared after a predetermined period of time such as the contract time (e.g., one year, etc.) has passed) may be incremented by, for example, one. When the event counter exceeds a predetermined threshold (e.g., four exits, etc.), the incentive to participate in the event or program may be lost, and / or the controller may be forced to automatically participate in the event until the next contract period. The controller may notify the operator of the following facts by an alarm (e.g., via a display device, sound, light indicator, etc.): (1) the event counter exceeds a predetermined threshold, (2) the event is requested, and / or (3) a predetermined set of rules or conditions are met in one embodiment, or are not met in another embodiment (causing the controller to automatically and / or fully or partially participate in the event). It should also be understood that the terms / clauses or contracts of some events or programs may allow for partial participation in the event. That is, even if the full demand of an event such as a demand reduction event cannot be met due to various reasons, participation may be allowed, but the contribution (e.g., to power reduction, to power consumption, etc.) is less than the demand. Contributions from multiple facilities can also be aggregated to meet the need for power reduction or consumption.

[0046] It should be appreciated that when participating in the event, the controller may control the assets (eg, electrical loads, etc.) of the facility to perform load shedding, such as by shutting off or reducing power to one or more assets. Processing may proceed from block 330 to block 340 .

[0047] At box 340 (Notify Operator), the controller may notify the operator of the facility of participation or exiting the event and / or any suitable statistical data, such as the number of times the operator and / or controller has exited previous events, the timestamp of the event, etc. It should be understood that for demand response events or plans, the operator and / or controller are notified of the event and can respond to such notification by joining or exiting. Depending on the terms of the contract, the ability of the operator or controller to exit the event may be limited. Features of the embodiments disclosed herein may also be applied to events associated with power plants such as virtual power plants and associated electrical loads. In some embodiments, the operator can override the decision to participate or exit made by the controller and / or automatic algorithm.

[0048] It should be understood that even if operators and / or controllers do not participate in a formal demand response program to obtain incentives associated with reducing demand based on requests, they may still be interested in reducing the energy intensity of their facilities to obtain economic and / or environmental benefits. Therefore, features of the embodiments disclosed herein can be applied to demand response programs or events and / or other events for reducing electrical energy consumption. In such embodiments, operators and / or controllers can define situations and consequences for reducing total energy use in a manner that optimizes the manufacturing process, the comfort of people in the space of the facility, and / or the cost of operating the facility.

[0049] Figure 4 A flow chart of a method 400 for controlling an electrical load from a load facility of an electrical power source is shown according to an embodiment.

[0050] It should be understood that, unless otherwise specified, the process flow 400 disclosed herein may be executed by one or more controllers or processors (including, for example, Figure 2 200 and / or processor 210, any other suitable processor, and / or combination thereof).

[0051] It should also be understood that the processing flow 400 may include one or more operations, actions, or functions illustrated by one or more of the boxes 410, 420, 430, and 440. These various operations, functions, or actions may correspond, for example, to software, program code, or program instructions executable by a processor that causes these functions to be performed. Although shown as discrete boxes, significant modifications may be made depending on the desired implementation, for example, two or more boxes may be reordered; additional boxes may be added; and various boxes may be divided into additional boxes, combined into fewer boxes, or removed. It should be understood that operations including initialization, etc. may also be performed before the processing flow 400. For example, system parameters and / or application parameters may be initialized. It should be understood that the features of the embodiments disclosed herein are applicable to demand response applications, virtual power plant applications, and / or other suitable applications. The processing flow 400 may begin at box 410.

[0052] At block 410 (Obtain Trend Data), the controller may obtain or collect historical data (e.g., trend data, etc.), such as resources (e.g., electrical loads, etc.), systems (e.g., building automation systems), etc. The trend data and / or the obtained trend data may be stored, for example, in a memory of the system, a cloud storage, etc.

[0053] In an embodiment, the controller may obtain trend data for a set of selected resources (e.g., electrical loads, etc.) that is predetermined or input by a user via, for example, a user interface (e.g., a graphical user interface). For details, see Figure 5 Processing may proceed from block 410 to block 420.

[0054] At block 420 (Evaluate Resources), the controller may evaluate or assess the application of each selected resource and / or combination thereof based on the trend data of the selected resources from block 410 to determine or predict (see block 430) the result, impact, or effect of applying the selected resource or combination of selected resources (e.g., turning off or reducing power to the selected resource or combination of selected resources in one embodiment, turning on or increasing power to the selected resource or combination of selected resources in another embodiment). In an embodiment, the processing of block 420 may be combined with the processing of block 430. Processing may proceed from block 420 to block 430.

[0055] At block 430 (Predict Outcome), the controller may determine, estimate, or predict an outcome, impact, and / or effect based on the evaluation of the application of the selected resource from block 420 (see, for details). Figure 5 ). In an embodiment, the processing of blocks 420 and / or 430 may be implemented as a deterministic algorithm. In another embodiment, the processing of blocks 420 and / or 430 may be implemented as a stochastic algorithm, such as a machine learning algorithm using a machine learning model.

[0056] In an embodiment, the controller may generate a predictive model such as a machine learning model (and store the generated model in a memory) for predicting energy consumption (and / or other results, impacts, and / or effects, see for details Figure 5 ). The controller may also train the machine learning model using training data, such as trend data for resources of the facility. The controller may also deploy a trained machine learning model for making predictions based on input data (e.g., newly selected or input resources, etc.) to predict the results, impacts, and / or effects of applying the input data. The controller may also obtain or collect (e.g., from a memory of the system or in the cloud), for example, new or additional trend data for resources, and / or retrain or fine-tune the trained machine learning model based on the new or additional trend data. Processing may proceed from box 430 to box 440.

[0057] At block 440 (Data Visualization), the controller may display the data on, for example, a display or user interface device (see Figure 2 ) (for details, see Figure 5 In an embodiment, the controller may also visualize trend data of the resource, for example on a display or user interface device.

[0058] It should be understood that, whether as part of a formal demand response program or to reduce the energy intensity of the facility (or in some embodiments increase electricity consumption), operators and / or controllers of the facility can select from available electrical loads, scenarios, and / or strategies to cut (or increase) electricity consumption. Knowing in advance which strategies may be most effective in terms of the results, impacts, and / or effects (such as reductions or increases in electricity consumption, etc.) of applying such electrical loads, scenarios, and / or strategies can facilitate operators and / or controllers in making informed and / or balanced decisions. In embodiments, the predicted results, impacts, and / or effects may include, for example, savings predictions, predictions of environmental outcomes, and the like. Based on historical trend data and using predictive models, the controller can present information and visualizations to the operator that predicts the impacts on processes and personnel in the building. For details, see. Figure 5 Description.

[0059] It should also be understood that the features in the embodiments disclosed herein provide the ability to predict the benefits and results of variable participation in demand response, power plants such as virtual power plants, and / or other energy intensity reduction (or consumption) programs and / or track historical results and savings achieved (e.g., via trend data). Trend data can be used in conjunction with predictive models (such as machine learning models or other types of models) to generate or improve predictive capabilities, and can be used to visualize the results to an operator. In an embodiment, the controller can visualize the energy intensity reduction measures taken over a configurable time period, the resulting comfort and / or process parameters, the resulting cost savings, and / or the resulting carbon dioxide equivalent emissions reduction (e.g., by comparing these results with the results of not taking any action or not taking any energy intensity reduction / consumption measures). In an embodiment, the controller can be used to create traceable scenarios, comparing actual results with configurable "what-if" scenarios, focusing on: "This is what would have happened" if the scenario was selected or configured for participation in the event.

[0060] Figure 5 A user interface sequence 500 is shown of a method for controlling an electrical load from a load facility of an electrical power source according to another embodiment.

[0061] It should be understood that unless otherwise specified, the user interface sequence 500 disclosed herein may be implemented by one or more controllers or processors (including, for example, Figure 2 200 and / or processor 210, any other suitable processor, and / or combination thereof).

[0062] It should also be understood that the user interface sequence 500 may include one or more interfaces, operations, actions, and / or functions illustrated by one or more of the boxes 510, 520, 530, and 540. These various interfaces, operations, functions, and / or actions may correspond, for example, to software, program code, or program instructions executable by a processor that causes these functions to be performed. Although shown as discrete boxes, significant modifications may be made depending on the desired implementation, for example, two or more boxes may be reordered; additional boxes may be added; and various boxes may be divided into additional boxes, combined into fewer boxes, or removed. It should be understood that operations including initialization, etc. may be performed prior to the user interface sequence 500. For example, system parameters and / or application parameters may be initialized. It should be understood that the features of the embodiments disclosed herein are applicable to demand response applications, virtual power plant applications, and / or other suitable applications. The user interface sequence 500 may begin at box 510.

[0063] At box 510 (Cutting Down Scenarios), the controller may generate a user interface to (1) visualize cutting down or reducing scenarios or strategies (or consumption scenarios or strategies in another embodiment), and (2) facilitate the operator of the facility to select, pick, or input one or more electrical loads or strategies as, for example, part of a scenario. It should be understood that each scenario or strategy may include a specific combination of one or more electrical loads of the facility, corresponding configurations of the electrical loads (e.g., set points, upper and / or lower limits, configurable parameters, thresholds, settings, etc.), and / or other necessary equipment and configurations. For example, as shown in box / interface 510, the operator may select three predetermined cutting down strategies from five predetermined cutting down strategies to evaluate or assess - indicated by a check box selector (or any other suitable selector) preceding each option.

[0064] In an embodiment, the controller may also provide a mechanism for the operator to confirm the selection, selection or input of one or more electrical loads or strategies or scenarios and / or proceed to the next step or next user interface. The user interface sequence may proceed from block 510 to block 520 .

[0065] At box 520 (Condition Information), the controller may generate a user interface to (1) visualize the condition information and (2) facilitate an operator of the facility to select, pick, or input one or more condition information (such as the day or date to be evaluated, ambient weather conditions (temperature, relative humidity, carbon dioxide levels, etc.), expected occupancy of the facility (e.g., number of personnel, etc.), planned manufacturing capacity (which may include information such as the amount of electricity / energy and / or number of personnel required to meet such capacity, etc.), predictions of cloud cover, and / or any other suitable factors that may contribute to the overall effectiveness of the strategy selected at box 510).

[0066] In an embodiment, the controller may also provide a mechanism for the operator to confirm the selection, choice or input of one or more condition information and / or proceed to the next step or next user interface. The user interface sequence may proceed from block 520 to block 530 .

[0067] At block 530 (Predictive Modeling), the controller may generate an optional user interface to (1) visualize the predictive model (e.g., a trained machine learning model, an untrained machine learning model, etc.), and (2) facilitate an operator of the facility to run the predictive model to predict the outcome of the scenario selected at block 510 (e.g., a selected electrical load or strategy, etc.), with the condition information provided at block 520 as an input to the predictive model, e.g., for participating in the event. In an embodiment, the controller may have trained the predictive model using historical data and / or historical scenario effectiveness (i.e., trend data for the scenario, etc.). In such an embodiment, the controller may run or execute the trained predictive model, using the scenario selected at block 510 (e.g., a selected electrical load or strategy, etc.) and with the condition information provided at block 520 as an input to the predictive model, to predict the outcome of the selected scenario using the provided condition information, e.g., for participating in the event. In another embodiment, the controller can provide trend data for the scenario as well as the selected scenario and condition information as input to a predictive model (e.g., an untrained model, a deterministic algorithm, etc.) to run or execute the predictive model to use the provided condition information to predict the outcome of the selected scenario, such as for participating in the event.

[0068] In an embodiment, the controller may also provide a mechanism for an operator to run or execute a predictive model and / or proceed to the next step or next user interface. The user interface sequence may proceed from box 530 to box 540. Where box / interface 530 is optional, the user interface sequence may proceed from box 520 to box 540.

[0069] At block 540 (Data Visualization), the controller may generate a user interface to (1) visualize a summary and / or overall impact of the selected scenario with condition information according to a number of key performance indicators, e.g., for participating in the event, and (2) facilitate an operator of the facility to view, review, and / or compare results of different scenarios. In an embodiment, the controller may also generate a user interface to (1) visualize trend data of the selected electrical load, e.g., for participating in past events, and (2) facilitate an operator of the facility to view, review, and / or compare different trend data of the selected electrical load.

[0070] In embodiments, performance indicators (i.e., predicted outcomes, impacts, or effects from block 530) may include, for example, cost savings (for participating in the event), carbon dioxide benefits (compared to taking no action on the event), space temperature, space relative humidity, space carbon dioxide levels, and / or manufacturing capacity, etc., for example, for participating in the event.

[0071] It should be understood that the controller can generate a user interface (not shown) to facilitate the operator of the facility to select, choose or input the preferred scenario as the final configuration, such as for participating in the event (e.g., a demand response event, an energy reduction or consumption event of a virtual power plant, and / or any other suitable event). The controller can also generate a user interface (not shown) to facilitate the operator of the facility to control the preferred scenario (e.g., control the electrical loads in the preferred scenario, such as for load shedding by shutting down or reducing the power supplied to one or more electrical loads, etc.).

[0072] In an embodiment, one or more devices of the HVACR system may be one or more electrical loads. It should be understood that HVACR equipment may be turned down (e.g., by reducing capacity, etc.) or shut down to reduce, for example, power or energy consumption, but occupant comfort may become an issue. Similarly, reduction strategies (e.g., power, energy, carbon dioxide levels, etc.) may affect business processes, such as manufacturing and / or production output. The predictive capabilities of the building automation system may use, for example, historical data to predict the impact of each scenario on people and processes in the facility. Features in the embodiments disclosed herein may help operators of a facility select from available electrical loads and / or scenarios when participating in (or exiting) an event to maximize cost savings while minimizing the impact on their facility.

[0073] It should also be understood that in conjunction with the historical and predictive capabilities of demand or event management functions in the building automation system, the operator and / or controller can define limits on the electrical loads and / or strategies that can be applied when participating in (or exiting) an event. In an embodiment, when curtailment is initiated and managed for energy reduction, the controller can monitor the resulting conditions or results. The operator and / or controller can define acceptable curtailment limits, and the controller can change the application of electrical loads and / or strategies accordingly, for example, between available electrical loads and / or strategies.

[0074] For example, in an embodiment, one of the reduction strategies is given to relax the space temperature heating and cooling set points in each space of the occupied building when participating in an event. Depending on the specific application, this change (i.e., relaxation) can result in overall cost savings, but may compromise occupant comfort. In such an embodiment, the operator and / or controller can define that the specific action (i.e., relaxation or participation in the event, etc.) can be released in lieu of a different strategy or restriction of electrical loads. For example, a change in space temperature can restrict the terminal equipment in each space to maintain comfort conditions. When the temperature and / or humidity in the space exceeds a user-defined threshold, the controller can release the temporary operation of the active set point and take different actions to reduce energy instead. This automatic change (i.e., releasing control of some electrical loads for load shedding, and / or selecting other electrical loads to control load shedding, etc.) can allow the building automation system to regain control of space conditions while minimizing the overall reduction in energy use.

[0075] That is, for example, when current conditions (e.g., temperature, humidity, etc.) exceed a threshold, the controller may release control of a first group of resources (which are selected to participate in the event, e.g., for load shedding), select a second group of resources from the available resources, and control the second group of resources, e.g., for load shedding.

[0076] In an embodiment, a priority (e.g., stored in a memory) may be provided for each scenario, each electrical load, and / or each operation / operation limit or threshold. An operator may define the priority of all available electrical loads and / or strategies and the operational limits for each electrical load and / or strategy during the reduction period. In such an embodiment, when a particular electrical load and / or strategy may result in a condition that falls outside an acceptable user-defined window or threshold, the controller may apply each electrical load and / or strategy while rotating the electrical loads and / or strategies accordingly. For example, an operator and / or controller may define an acceptable space comfort condition, outside of which the associated electrical load reduction strategy may be released in favor of other options. Similar restrictions may be imposed on other reduction strategies to ensure that personnel and processes in the facility remain within acceptable limits. That is, for example, when current conditions (e.g., temperature, humidity, etc.) exceed a threshold, the controller can release control of a first group of resources (which were selected to participate in the event, e.g., for load shedding) and rotate control within or between available resources and / or strategies (e.g., select a second group of resources, control the second group of resources, e.g., for load shedding, and then release control of the second group of resources, select a third group of resources, control the third group of resources, e.g., for load shedding, and then whenever the current conditions fall outside of an acceptable user-defined threshold, release control of the second group of resources, select a third group of resources, control the third group of resources, e.g., for load shedding, etc.).

[0077] It should be understood that the disclosed and other solutions, examples, embodiments, modules and functional operations described in this document can be implemented in digital electronic circuits or in computer software, firmware or hardware (including the structures disclosed in this document and their structural equivalents) or in a combination of one or more of them. The disclosed and other embodiments can be implemented as one or more computer program products (i.e., one or more modules of computer program instructions) encoded on a computer-readable medium for execution by a data processing device or for controlling the operation of a data processing device. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a material composition that affects a machine-readable propagation signal, or a combination of one or more of them. The term "data processing device" includes all devices, devices and machines for processing data, exemplarily including a programmable processor, a computer, or multiple processors or computers. In addition to hardware, the device may also include code that creates an operating environment for the computer program in question, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0078] Computer programs (also referred to as programs, software, software applications, scripts or codes) can be written in any form of programming language, including compiled or interpreted languages, and computer programs can be deployed in any form, including deployment as stand-alone programs or deployment as modules, components, subroutines or other units suitable for use in a computing environment. Computer programs do not need to correspond to files in a file system. Programs can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple collaborative files (e.g., files storing one or more modules, subroutines or code portions). Computer programs can be deployed to execute on a computer or on multiple computers located at a site or distributed at multiple sites and interconnected by a communication network.

[0079] The processes and logic flows described in this document may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating input data and generating output. These processes and logic flows may also be performed by a dedicated logic circuit (e.g., a field programmable gate array or an application specific integrated circuit, etc.), and the apparatus may also be implemented as the dedicated logic circuit.

[0080] Processors suitable for executing computer programs include, for example, both general-purpose microprocessors and special-purpose microprocessors and any one or more processors of any type of digital computer. Typically, the processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) for storing data, or be operably coupled to receive data from or send data to the one or more mass storage devices. However, the computer does not need to have these devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices (e.g., erasable programmable read-only memory, electrically erasable programmable read-only memory, and flash memory devices); magnetic disks (e.g., built-in hard disks or removable disks); magneto-optical disks; and compact disk read-only memory and digital video disk read-only memory disks. The processor and memory can be supplemented by or incorporated into a dedicated logic circuit.

[0081] It should be understood that different features, variations and multiple different embodiments have been shown and described in various details. The content sometimes described in the form of specific embodiments in this application is only for illustrative purposes and is not intended to limit or imply that the content that has been envisioned is only a specific embodiment or specific embodiment. It should be understood that the present disclosure is not limited to any single specific embodiment or enumerated variation. Those skilled in the art will think of many modifications, variations and other embodiments, and these modifications, variations and other embodiments are intended to and are actually covered by the present disclosure. In fact, the scope of the present disclosure should be determined by those skilled in the art according to the appropriate legal interpretation and construction (including equivalents) of the present disclosure as understood by the complete disclosure that exists when submitting.

[0082] aspect:

[0083] It will be understood that any of these aspects may be combined with each other.

[0084] Aspect 1. A building automation system for controlling an electrical load from a load facility on an electrical grid using demand response, the system comprising:

[0085] a memory configured to store a plurality of predetermined conditions; and

[0086] Controller;

[0087] The controller is configured as follows:

[0088] receiving a request for a demand response event,

[0089] Determine multiple current conditions,

[0090] Automatically participate in or exit demand response events based on current conditions and predetermined conditions, and

[0091] At a minimum, the operator responsible for the demand response event is notified upon exiting the demand response event.

[0092] Aspect 2. The system according to aspect 1, wherein the predetermined condition includes a first threshold, the current condition includes a first condition, and the controller is further configured to:

[0093] When the first condition is equal to or less than a first threshold, automatically participate in a demand response event, and

[0094] Control a group of electrical loads for load shedding.

[0095] Aspect 3. The system according to aspect 2, wherein the memory is further configured to store an exit counter and an exit threshold, and the controller is further configured to:

[0096] automatically exiting the demand response event when the first condition is greater than a first threshold and when the exit counter is equal to or less than the exit threshold; and

[0097] Increment the exit counter.

[0098] Aspect 4. The system according to aspect 3, wherein the controller is further configured to:

[0099] When the exit counter is greater than the exit threshold, it automatically participates in the demand response event.

[0100] Control the group of electrical loads to perform load shedding, and

[0101] The operator is notified via an alarm.

[0102] Aspect 5. The system according to any one of aspects 1 to 4, wherein the memory is further configured to store a predetermined schedule, and the controller is further configured to:

[0103] Automatically participate in demand response events when the current schedule meets the predetermined schedule.

[0104] Aspect 6. The system according to any one of aspects 1 to 5, wherein the memory is further configured to store a predetermined schedule, and the controller is further configured to:

[0105] When the current schedule meets the predetermined schedule, the demand response event is automatically exited.

[0106] Aspect 7. The system according to any one of aspects 1 to 6, wherein the memory is further configured to store a priority of each electrical load, and the controller is further configured to:

[0107] determining a group of electrical loads based on the power demand of the demand response event, the power demand of each electrical load, and the priority of each electrical load; and

[0108] When participating in a demand response event, a determined group of electrical loads are controlled to perform load shedding.

[0109] Aspect 8. A method for controlling an electric load from a load facility of an electric power source, the method comprising:

[0110] identifying a set of electrical loads;

[0111] Identify a set of conditions;

[0112] predicting energy consumption using a machine learning model based on the set of electrical loads and the set of conditions;

[0113] Visualizing the energy consumption on a display device; and

[0114] The set of electrical loads is controlled under the set of conditions.

[0115] Aspect 9. The method according to aspect 8 further includes:

[0116] Obtaining a trend data set of the group of electrical loads;

[0117] Use trending datasets to train machine learning models; and

[0118] Deploy a trained machine learning model to predict this energy consumption.

[0119] Aspect 10. The method according to aspect 9 further comprises:

[0120] Visualize trended datasets on display devices.

[0121] Aspect 11. A method according to any one of Aspects 8 to 10, wherein visualizing energy consumption on a display device includes visualizing cost savings, carbon dioxide benefits, space temperature, space relative humidity, space carbon dioxide level, and manufacturing capacity corresponding to the predicted energy consumption.

[0122] Aspect 12. The method according to any one of aspects 8 to 11, further comprising:

[0123] Identify a set of energy reduction scenarios;

[0124] Using machine learning models to predict energy reductions for energy reduction scenarios; and

[0125] The energy reduction of each of the energy reduction scenarios is compared, and based on the comparison, a first energy reduction scenario is selected among the energy reduction scenarios.

[0126] Aspect 13. The method according to aspect 12, wherein a priority is provided for each of the energy reduction scenarios, and the first energy reduction scenario has the highest priority among the energy reduction scenarios.

[0127] Aspect 14. The method according to any one of aspects 8 to 13, wherein a priority is provided for each of the electrical loads.

[0128] Aspect 15. The method according to any one of aspects 12 to 14, wherein the first energy reduction scenario includes the set of electrical loads and the set of conditions.

[0129] Aspect 16. The method according to aspect 15, wherein the set of conditions includes a first condition, and the method further comprises:

[0130] releasing control of the resource in the first energy reduction scenario; and

[0131] When the first condition exceeds a first threshold, selecting a second energy reduction scenario among the energy reduction scenarios; and

[0132] Controlling resources in a second energy reduction scenario.

[0133] Aspect 17. The method according to aspect 15 or aspect 16, wherein the set of conditions includes a first condition, and the method further comprises:

[0134] When the first condition exceeds a first threshold, control is rotated in an energy reduction scenario.

[0135] Aspect 18. The method according to any one of aspects 8 to 17, wherein controlling the group of electrical loads includes shutting off or reducing power supplied to one or more of the electrical loads.

[0136] Aspect 19. A building automation system for controlling an electrical load from a load facility of an electrical power source, the system comprising:

[0137] a memory configured to store the machine learning model; and

[0138] Controller;

[0139] The controller is configured as follows:

[0140] identifying a set of electrical loads;

[0141] Identify a set of conditions;

[0142] predicting energy consumption using a machine learning model based on the set of electrical loads and the set of conditions;

[0143] Visualizing the energy consumption on a display device; and

[0144] The set of electrical loads is controlled under the set of conditions.

[0145] Aspect 20. The system according to aspect 19, wherein the controller is further configured to:

[0146] Obtaining a trend data set of the group of electrical loads;

[0147] Use trending datasets to train machine learning models;

[0148] Deploy a trained machine learning model to predict this energy consumption.

[0149] The terms used in this specification are intended to describe specific embodiments and are not intended to be limiting. Unless otherwise expressly indicated, the terms "a", "an" and "the" also include plural forms. The terms "comprise" and / or "include" when used in this specification specify the presence of stated features, wholes, steps, operations, elements and / or components, but do not exclude one or more other features, wholes, steps, operations, elements and / or components.

[0150] With respect to the foregoing description, it should be understood that changes in detail may be made without departing from the scope of the present disclosure, especially in the construction materials adopted and the shapes, sizes and arrangements of the parts. This specification and the described embodiments are exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A building automation system for controlling an electrical load from a load facility on an electrical grid using demand response, the system comprising: a memory configured to store a plurality of predetermined conditions; as well as Controller; Wherein, the controller is configured as: Receive a request for a demand response event, Determine multiple current conditions, automatically participate in or exit the demand response event based on the current conditions and the predetermined conditions, and An operator responsible for the demand response event is notified at least upon exiting the demand response event.

2. The system according to claim 1, wherein: The predetermined condition includes a first threshold, the current condition includes a first condition, and the controller is further configured to: When the first condition is equal to or less than the first threshold, automatically participate in the demand response event, and Control a group of electrical loads for load shedding.

3. The system according to claim 2, wherein: The memory is further configured to store an exit counter and an exit threshold, and the controller is further configured to: When the first condition is greater than the first threshold and when the exit counter is equal to or less than the exit threshold, automatically exiting the demand response event; and The exit counter is incremented.

4. The system according to claim 3, wherein: The controller is also configured to: When the exit counter is greater than the exit threshold, automatically participate in the demand response event, controlling the set of electrical loads to perform load shedding, and The operator is notified by an alarm.

5. The system according to claim 1, wherein: The memory is further configured to store a predetermined schedule, and the controller is further configured to: When the current schedule meets the predetermined schedule, the demand response event is automatically participated in.

6. The system according to claim 1, wherein: The memory is further configured to store a predetermined schedule, and the controller is further configured to: When the current schedule meets the predetermined schedule, the demand response event is automatically exited.

7. The system according to claim 1, wherein: The memory is further configured to store a priority of each electric load, and the controller is further configured to: determining a group of electrical loads based on the power demand of the demand response event, the power demand of each electrical load, and the priority of each electrical load; and When participating in the demand response event, a determined group of electric loads are controlled to perform load shedding.

8. A method for controlling an electrical load from a load facility of an electrical power source, the method comprising: identifying a set of electrical loads; Identify a set of conditions; predicting energy consumption using a machine learning model based on the set of electrical loads and the set of conditions; visualizing said energy consumption on a display device; as well as The set of electrical loads is controlled under the set of conditions.

9. The method according to claim 8, further comprising: obtaining a trend data set of the set of electrical loads; Using the trending dataset to train the machine learning model; as well as A trained machine learning model is deployed to predict the energy consumption.

10. The method according to claim 9, further comprising: The trend data set is visualized on the display device.

11. The method according to claim 8, wherein: Visualizing the energy consumption on the display device includes visualizing cost savings, carbon dioxide benefits, space temperature, space relative humidity, space carbon dioxide level, and manufacturing capacity corresponding to the predicted energy consumption.

12. The method according to claim 8, further comprising: Identify a set of energy reduction scenarios; using the machine learning model to predict energy reduction for the energy reduction scenario; as well as The energy reduction of each of the energy reduction scenarios is compared, and based on the comparison, a first energy reduction scenario is selected among the energy reduction scenarios.

13. The method according to claim 12, wherein: A priority is provided for each of the energy reduction scenarios, the first energy reduction scenario having a highest priority among the energy reduction scenarios.

14. The method according to claim 12, wherein: A priority level is provided for each of the electrical loads.

15. The method according to claim 12, wherein: The first energy reduction scenario includes the set of electrical loads and the set of conditions.

16. The method according to claim 15, wherein: The set of conditions includes a first condition, the method further comprising: releasing control of resources in said first energy reduction scenario; and When the first condition exceeds a first threshold, selecting a second energy reduction scenario among the energy reduction scenarios; and Controlling resources in the second energy reduction scenario.

17. The method according to claim 15, wherein: The set of conditions includes a first condition, the method further comprising: When the first condition exceeds a first threshold, control is switched in the energy reduction scenario.

18. The method according to claim 8, wherein: Controlling the set of electrical loads includes shutting off or reducing power to one or more of the electrical loads.

19. A building automation system for controlling an electrical load from a load facility of a power source, the system comprising: a memory configured to store the machine learning model; as well as Controller; Wherein, the controller is configured as: identifying a set of electrical loads; Identify a set of conditions; predicting energy consumption using a machine learning model based on the set of electrical loads and the set of conditions; Visualizing the energy consumption on a display device; and The set of electrical loads is controlled under the set of conditions.

20. The system of claim 19, wherein: The controller is also configured to: obtaining a trend data set of the set of electrical loads; Using the trending dataset to train the machine learning model; A trained machine learning model is deployed to predict the energy consumption.

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