RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

The diesel generators sets (DGs) and battery storage systems (BSS) are the essential energy sources in a modern high-rise buildings. In this paper DG, BSS and Photovoltaic system (PV) has been considered to minimize the grid power injection using a centralized Energy Management System (EMS). Machine Learning (ML) techniques are used to predict the performance of various regression models by comparing grid power and load curves. It includes Artificial Neural Network (ANN), Wide Neural Network (WNN), Linear Regression (LR), Linear Regression Interaction (LR-I), Linear Regression Stepwise (LR-S), Regression Fine Tree (RF-T), Regression Coarse Tree (RC-T) and Gaussian Process Regression (GPR) based techniques. The Demand Side Management (DSM) techniques such as peak shaving and valley filling is integrated with ML technique in a Hybrid energy source (HS) system.The comparative analysis of results depicts the effective reshaping of the grid profile without scheduling or disconnecting the loads. Matlab simulation software is used to validate the results.

Automated summary

This paper considers diesel generators sets (DGs), battery storage systems (BSS) and Photovoltaic system (PV) in a centralized Energy Management System (EMS) to minimize grid power injection. Machine learning (ML) techniques are used to predict performance of regression models like ANN, WNN, LR, LR-I, LR-S, RF-T, RC-T, and GPR. Demand Side Management (DSM) techniques like peak shaving and valley filling are integrated with ML technique in a Hybrid energy source (HS) system. Results show effective reshaping of grid profile without load scheduling or disconnection, validated using Matlab simulation software.