Performance Improvement of Smart Grid by Implementing Hybrid Load Forecasting Method Using Neural Networks

Abstract

The increasing use of renewable energy resources in power systems worldwide has led to the development of smart grids, which allow for bidirectional communication between supply and demand. Accurate load forecasting is crucial for maximizing the efficiency of these smart grids, as it allows for the switching between renewable and other energy resources, reducing overall costs and transmission losses. Neural network-based load forecasting models are performing better than traditional statistical models. The hybridization of neural network-based models with optimized learning algorithms is the focus of our research. In this work, we propose a novel approach for load forecasting in smart grids. The approach includes four key components: refactoring the smart grid dataset for load forecasting, gathering a highly relevant set of parameters affecting domestic load, constructing four hybrid models with appropriate tuning parameters, and comparative analysis of these models. We devise proper format and structure the data used for load forecasting, ensuring that it is in the best possible form for accurate predictions. We carefully select the most important parameters for medium and short-term load forecasting, ensuring that the model is able to take into account the most relevant factors. We use an empirical approach to determine the best set of parameters for our models, ensuring that they are optimized for accurate predictions.We have modelled three machine learning hybrid models with ANN, hybridized with genetic algorithm, particle swarm optimization, and Levenberg Marquardt, along with one deep learning model RNN-LSTM. Our comparative results show that ANN-GA and ANN-PSO performed better in medium-term forecast with accuracy rates of 93.7 and 98.26 percent, respectively. While ANN-LM and RNN LSTM performed better in short-term forecast achieving accuracy rates of 97 and 99 percent, respectively. Our results demonstrate that our proposed approach is effective in achieving highly accurate load forecasts, making it a valuable tool for optimizing the performance of smart grids. i