Residential Load Forecasting and Dynamic Demand Side Management

Abstract

This paper optimizes home energy consumption using advanced load forecasting and dynamic demand side management (DSM) approaches for Bangladesh's changing energy landscape. Grid resilience, peak demand reduction, and energy efficiency are priorities. Long Short-Term Memory (LSTM) neural networks use historical data and weather variables to forecast energy usage. Univariate and multivariate analyses are performed, with multivariate models predicting load needs better. The research examines load-shifting solutions to balance energy usage and reduce grid stress during peak periods. This study assesses the cost-effectiveness of DSM interventions. The research shows that time-of-use tariffs and optimization algorithms can reduce costs and enhance load factors. A comprehensive literature evaluation highlights existing DSM and load forecasting methodologies and identifies critical research gaps. Optimized load-shifting lowers consumer prices and improves energy grid stability and efficiency. The findings imply that enhanced forecasting and DSM tactics can boost economic and environmental performance. The study stresses the need to use current engineering tools and approaches to produce sustainable energy solutions that promote ethical and social well-being. Policymakers and suppliers seeking energy sustainability and resilience in Bangladesh can learn from this research.