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: The present disclosure provides a method and a system for estimating capacity and usage pattern of behind-the-meter energy storage in electric networks. Conventional techniques on estimating an effective capacity of behind-the-meter energy storage of a consumer, in presence of distributed energy generation units is limited, computationally intensive and provide inaccurate prediction. The present disclosure provides an accurate estimate of the effective capacity and usage pattern of behind-the-meter energy storage of a target consumer utilizing data samples received from a utility in presence of one or more distributed energy generation units, using an energy balance equation with less computation and accurate prediction. Based on accurate estimation of the effective capacity and usage pattern, the utility may plan for proper infrastructure to meet power demands of the consumers.

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: 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 generally to the methods and systems for fault detection, diagnosis and localization in solar panel network. Conventional fault detection and diagnosis (FDD) techniques for the solar panel network are limited and confined to identifying faults either at voltage level or current level, or to studying one specific fault type at a time. The present disclosure solve the problems of detecting various fault types present inside the solar panel network and identifying associated fault locations, by generating a fault detection, diagnosis and localization (FDDL) model. The convolutional neural network (CNN) model is trained with fault datasets and no-fault datasets covering various fault scenarios and no-fault scenarios respectively, to generate the FDDL model. The plurality of fault datasets and the plurality of no-fault datasets are determined based on the network simulation model of the solar panel network.

Abstract: The present disclosure provides a method and a system for estimating capacity and usage pattern of behind-the-meter energy storage in electric networks. Conventional techniques on estimating an effective capacity of behind-the-meter energy storage of a consumer, in presence of distributed energy generation units is limited, computationally intensive and provide inaccurate prediction. The present disclosure provides an accurate estimate of the effective capacity and usage pattern of behind-the-meter energy storage of a target consumer utilizing data samples received from a utility in presence of one or more distributed energy generation units, using an energy balance equation with less computation and accurate prediction. Based on accurate estimation of the effective capacity and usage pattern, the utility may plan for proper infrastructure to meet power demands of the consumers.

Abstract: A method and system are provided for determining at least one combined forecast value of non-conventional energy resources. An Input/output Interface receives an adaptively selected historical dataset and a current dataset from one or more predictive forecast models and/or measurements. An adaptive forecast module generates one or more variants of machine learning models to model the performance of the one or more predictive forecast models by training the one or more variants of machine learning models on the historical dataset. The adaptive forecast module correlates the current dataset with the historical dataset to adaptively obtain a filtered historical dataset. The adaptive forecast module evaluates the one or more variants of machine learning models on the filtered historical dataset. The adaptive forecast module derives a statistical model to determine the at least one combined forecast value by combining outputs obtained based on the evaluation.

Abstract: A method and system are provided for determining at least one combined forecast value of non-conventional energy resources. An Input/output Interface receives an adaptively selected historical dataset and a current dataset from one or more predictive forecast models and/or measurements. An adaptive forecast module generates one or more variants of machine learning models to model the performance of the one or more predictive forecast models by training the one or more variants of machine learning models on the historical dataset. The adaptive forecast module correlates the current dataset with the historical dataset to adaptively obtain a filtered historical dataset. The adaptive forecast module evaluates the one or more variants of machine learning models on the filtered historical dataset. The adaptive forecast module derives a statistical model to determine the at least one combined forecast value by combining outputs obtained based on the evaluation.