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

Abstract

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.

Recent Trends and Issues of Energy Management Systems Using Machine Learning

“…This methodology is characterized not only by its capability to enable optimal energy scheduling but also by its substantial reduction in communication overhead while maintaining stringent data privacy. A regression analysis-based DSMS has been proposed in the centralized EMS framework for minimizing grid power injection and maximizing the efficiency of hybrid energy sources [96].…”

Techno-Economic Comparative Analysis of Grid-Connected and Islanded Hybrid Renewable Energy Systems in 7 Climate Regions, Turkey

“…In addition to the studies referred to in [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], and [64], there are also studies in which artificial intelligence is included in the effective use of energy efficiency and renewable energy resources. A centralized energy management system (EMS) and machine learning models were employed to optimize the use of PV, DG, and BESS, with the goal of minimizing grid power injection and maximizing the usage of HRES [65]. The results showed that Regression Coarse Tree and Linear Regression methods give better results than other machine learning techniques in reducing peak demand and maximizing the utilization of HRES.…”

Predicting renewable energy production outputs from climate factors: A machine learning approach

“…Bilgic et al (2023) reviewed artificial neural network advances in hydrogen production research [22]. Asghar et al (2022) integrate demand side management techniques with various machine learning methods in a hybrid energy source system [23]. Yao et al (2023) introduce key performance indicators for the comparison of machine learning workflows for energy research and evaluate the advances in the application of machine learning methods in energy management, storage, harvesting, and conversion [24].…”