SHORT-TERM LOAD FORECASTING IN POWER SYSTEMS USING DEEP LEARNING ALGORITHMS

Abstract

Short-Term Load Forecasting (STLF) plays a crucial role in power system planning and operation, as it helps utilities to efficiently allocate their resources and ensure reliable service to customer. In this project the performance of different forecasting algorithms such as Long Short-Term Memory (LSTM), Particle Swarm Optimization-Gated Recurrent Unit (PSO GRU), Multivariate LSTM, and 1-Dimensional Convolution Neural Network-Long Short Term Memory (1-D CNN LSTM) are evaluated. A widely used benchmark dataset namely Global Energy Forecasting Competition (GEFCOM) dataset is used in this work for training and performance validation. The performance of different load forecasting models is compared using performance indices like accuracy and Mean Absolute Percentage Error (MAPE). Among the different models used for short-term load forecasting, Multivariate LSTM model is found to be more accurate than other models. The results indicate that Multivariate LSTM is a promising approach for STLF, and its superior performance is attributed to its ability to handle multiple input variables. The study highlights the importance of model selection in accurate load forecasting and demonstrates the potential of Multivariate LSTM for STLF. The findings can help power system planners and operators to choose an appropriate STLF algorithm based on their specific needs and requirements