Abstract: This disclosure relates generally to a bidirectional charging at an electric vehicle (EV) charging station by an energy model that uses electricity bought from the day-ahead market for charging the fleet of electric vehicles (EVs) and uses the intra-day market for arbitrage. The competitive pricing of wholesale electricity markets and distributed energy resource capability of EV fleets (in addition) provide a revenue channel through energy arbitrage. To effectively handle electricity price variations and the energy demand of the EV fleet, the present disclosure utilizes a graph representation-based learning agent (LA3_D) with two-stage encoding for day-ahead charge planning; and a priority order based greedy heuristic (GH_I) for intra-day arbitrage planning. Because the agent learns the planning policy of mapping EVs to charging operations over several problem instances, it is able to solve a given instance with limited sub-optimality when put to test at different levels of scale.

Abstract: Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.

Abstract: Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance.

Abstract: This disclosure relates generally to method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. The method disclosed processes a received user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services and for the user request a graph is generated. Further, the trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with the delivery environment and obtains feedback for every associated action.

Abstract: Use of Physics Informed Neural networks (PINNs) to control building systems is non-trivial, as basic formalism of PINNs is not readily amenable to control problems. Specifically, exogenous inputs (e.g., ambient temperature) and control decisions (e.g., mass flow rates) need to be specified as functional inputs to the neural network, which may not be known a priori. The input feature space could be very high dimensional depending upon the duration (monthly, yearly, etc.) and the (min-max) range of the inputs. The disclosure herein generally relates to Heating, Ventilation, and Air-Conditioning (HVAC) equipment, and, more particularly, to method and system for physics aware control of HVAC equipment. The system generates a neural network model based on a plurality of exogeneous variables from the HVAC. The generated neural network model is then used to generate the one or more control signal recommendations, which are further used to control operation of the HVAC.

Abstract: Considering the dependency of a flight hold time on multitude of dynamically varying factors, determining an optimal hold time balancing between passenger utility and airline utility is challenging. State of art approaches are limited to use of only deterministic approaches with limited ML assistance that require huge labelled training data. Embodiments disclosed herein provide a method and system for computing and recommending optimal hold time for every flight of an airline so as to minimize passenger misconnects in airline operations through Reinforcement Learning (RL). The method disclosed utilizes RL, which is trained to make decision at a flight level considering local factors while still adhering to the global objective based on global factors. Further method introduces business constants in the rewards to the RL agents bringing in airline specific flexibility in reward function.

Abstract: Disadvantage of state-of-the-art scheduling mechanisms for Electric Vehicle (EV) charging is that they fail to accommodate dynamic requirements in terms of charging needs. The disclosure herein generally relates to EV fleet charging, and, more particularly, to a method and system for Electric Vehicle (EV) fleet charging by accommodating one or more dynamic requirements. The system initially generates a base charging plan for a fleet of EVs. Further, the system checks if the base charging plan is to be modified to accommodate one or more dynamic charging requirements obtained. Upon determining that the base charging plan is to be modified, the system modifies the base charging plan till a) no more vehicles are left to charge, or b) all of a plurality of chargers have an assignment.

Abstract: This disclosure relates generally to method and system for maximizing space utilization in a building. Due to current pandemic scenario many organizations eventually need to plan for the return of employees to office space ensuring biosafety. The challenge of maximizing the office space utilization ensuring occupants biosafety and comfort thereby minimizing HVAC energy consumption is necessary. The method utilizes two heuristic approaches for determining maximum allowable occupants placement in the open plan space using an optimal occupant placement technique. This minimizes the HVAC energy if the actual count is lesser than the possible maximum occupants can be placed which further optimizes energy using a joint actuator control technique. Additionally, the proposed two heuristic approaches improve space utilization for the infection rate ensuring bio safety.

Abstract: This disclosure relates generally to method and system for detecting on-street parking violations. The method include capturing, by using an media capturing device embodied in an electronic device mounted in a vehicle, a video stream of a scene during a trip of the vehicle. The video stream is processed at the electronic device to identify target objects such as no-parking signage and vehicles parked in the vicinity thereof. A meta-information associated with the target objects is stored in form of a short-term historian in a repository associated with the electronic device. The absolute locations of the target objects are determined and the historian is updated with the values of the absolute locations. A set of unique target objects is determined from amongst the target objects and a meta-information associated with the unique objects is sent to a cloud server for determining parking violations.

Abstract: Vehicle-to-Grid (V2G) technologies are being adopted to reduce peak demand and to take over as energy sources during grid instability. It is necessary to estimate attributes of electric vehicle trips and residual battery charge in order to correctly predict spatio-temporal availability of energy from EVs to form a micro grid. However, it may not be feasible to get the required attributes for all vehicles in a city-scale traffic scenario. Embodiments of the present disclosure and system address the problem of accurately estimating the local energy reserve that is available from parked EVs during a given time of the day. In addition, the system also determines which neighborhoods have the potential to form micro grids using the parked EVs during a given time period. This will help grid operator(s) to plan and design smart grids which can create EV-powered micro grids in neighborhoods during periods of peak demand or during disruptions.

Abstract: Traditionally, benchmarking of asset performance involves comparing actual performance with ideal values that correspond to test conditions which may not be realized in practice leading to inappropriate ranking of the assets. Systems and methods of the present disclosure use condition-aware reference curves for estimating the maximum possible operating efficiencies (under specific operating conditions) instead of the theoretical maximum efficiencies. The reference curves are received from the manufacturer or obtained from on-site test results. Benchmarking is then performed based on two dimensions, viz., an inter-asset metric and an intra-asset metric that are analogous to the first law and second law of thermodynamics respectively. The two-dimensional benchmarking then helps in identifying inefficient assets that may be analyzed further for finding the root cause.

Abstract: This disclosure relates generally to method and system to generate pricing for charging electric vehicles. With Electric vehicles becoming more mainstream, public chargers may not be able to match demand supply without extensive deployments. The present disclosure dynamically generates pricing policies to maximize aggregator revenue based on a stochastic model constraints and user behavioral models. The system involves three primary stake holders comprising a demand side having EV users requesting efficient charging at lowest possible price, a supply side which includes a public or private EVSE operators, and an EV charging aggregator which acts as intermediator between the demand side and the supply side. The EV charging aggregator having an RL agent receives user requests to generate pricing based on a tentative demand pool, an actual demand pool, and a service pool. The reward to the RL agent is the total revenue obtained in that timestep of the control action.

Abstract: This disclosure relates generally to method and system for maximizing space utilization in a building. Due to current pandemic scenario many organizations eventually need to plan for the return of employees to office space ensuring biosafety. The challenge of maximizing the office space utilization ensuring occupants biosafety and comfort thereby minimizing HVAC energy consumption is necessary. The method utilizes two heuristic approaches for determining maximum allowable occupants placement in the open plan space using an optimal occupant placement technique. This minimizes the HVAC energy if the actual count is lesser than the possible maximum occupants can be placed which further optimizes energy using a joint actuator control technique. Additionally, the proposed two heuristic approaches improve space utilization for the infection rate ensuring bio safety.

Abstract: Electrical utilities offer incentives to customers to reduce consumption during periods of demand-supply mismatch. A building's participation in demand response (DR) depends both on its ability (due to building constraints), and its willingness (a function of incentive) to reduce electricity. Customers prefer a large incentive whereas a utility would want to minimize the revenue outflow to achieve a target reduction. Systems and methods of the present disclosure identify optimal incentive from the utility's perspective reflecting this trade-off. A model is built to estimate the demand response potential (DRP) of a building for a given incentive offered by the utility. The models for individual buildings are used to characterize the behavior of an ensemble of buildings. The utility may then decide optimum incentives that should be offered to achieve a target DR, using the associated DRP.

Abstract: The present disclosure provides system and method for determining optimal decision parameters for a demand response (DR) event involving a District Cooling Plant (DCP). Most of conventional DR event techniques address control of building-level energy consumption loads alone while in presence of District Cooling (DC) has not received much attention when a plurality of buildings are served by a District Cooling Plant (DCP). The disclosed system and method determine set points of optimal decision parameters of the plurality of buildings and the DCP, by conditioning and un-conditioning on the DCP parameters such that a thermal discomfort of occupants residing in the plurality of buildings is minimum and achieves a maximum target energy demand reduction during the DR event. The disclosed system and method work for hundreds of buildings and able to determine the optimal decision parameters for each building and the DCP efficiently.

Abstract: Various fault types occurring at multiple possible locations in the solar panel network are simulated using the network simulation model. The dataset covering multiple fault scenarios and multiple no-fault scenarios is determined for training the CNN model. The fault scenarios include one fault type alone at particular location or multiple locations, as well as multiple fault types at multiple locations. The fault types include a short circuit fault, an open circuit fault, a shading fault, a soiling fault, a hot-spot fault, an arc fault, a degradation fault, and a clipping fault, the short circuit fault comprises a line-line fault, and a line-ground fault The convolutional neural network (CNN) model is trained with fault datasets and no-fault datasets covering various fault sensors and no-fault scenarios to generate the FDDL model. The fault datasets and no-fault datasets are determined based on the network simulation model of the solar panel network.

Abstract: This disclosure relates to a system and method for computing and recommending optimal hold time for every flight of an airline to minimize passenger misconnects in airline operations. The method recommends an optimal hold time for a flight based on passenger and airline disutility. Passenger disutility is computed based on the delay to destination of some or all of the passengers, availability of alternate routes for the connecting passengers, and a passenger specific connection time for connecting passengers. Airline disutility is computed based on the arrival delay of the outbound flight and subsequent flights of the same physical aircraft, till the delay no-longer propagates and an operating cost to the airline such as rebooking cost, ground cost etc. Finally, the method introduces a business factor, bringing in airline specific flexibility, to combine passenger and airline disutility to form total disutility and recommends the optimal hold based on least value.

Abstract: Considering the dependency of a flight hold time on multitude of dynamically varying factors, determining an optimal hold time balancing between passenger utility and airline utility is challenging. State of art approaches are limited to use of only deterministic approaches with limited ML assistance that require huge labelled training data. Embodiments disclosed herein provide a method and system for computing and recommending optimal hold time for every flight of an airline so as to minimize passenger misconnects in airline operations through Reinforcement Learning (RL). The method disclosed utilizes RL, which is trained to make decision at a flight level considering local factors while still adhering to the global objective based on global factors. Further method introduces business constants in the rewards to the RL agents bringing in airline specific flexibility in reward function.

Abstract: Conventionally, chiller power consumption has been optimized by using Cooling Load based Control (CLC) approach which does not consider impact of a control strategy on other. Embodiments of the present disclosure provide reinforcement learning based control strategy to perform both chiller ON/OFF sequencing as well as setpoint leaving chilled water temperature (LCWT) scheduling. A RL agent is trained using a re-trained transfer learning (TL) model and LCWT, return chilled water temperature of target chillers and ambient temperature of building are read for determining required cooling load to be provided by target chiller(s) based on which target chillers are scheduled for turning ON/OFF. Transfer learning-based approach is implemented by present disclosure to predict power consumed by a chiller at some setpoint by using a model trained on similar chillers which were operated at that setpoint since chillers are usually run at a single setpoint.

Abstract: Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance.