A hybrid extreme learning machine and sine cosine algorithm model for accurate electricity price forecasting

Abstrak

Electricity demand is continually rising due to the advancement of new technology, the switch to greener energy, and the popularity of electric vehicles over conventional ones. The proliferation of businesses in the generation and distribution sectors has increased competition in the electricity market. Forecasting electricity prices enables consumers to control their monthly electricity bills and consumer-owned distributed generation by knowing the forecasted hourly price. For demand management, generation scheduling, and bidding price quotations, electricity price forecasting is crucial for buyers, generation businesses, and bidders alike. Electricity price data is highly nonlinear and affected by numerous factors because of which EPF models are more complex, highly volatile and slow in convergence. A range of neural network models, training algorithms, and hybrid systems comprising two or more models have been suggested for precise and efficient electricity price forecasting by researchers over the decade. This study involves the development of a hybrid neural network model with two intelligent algorithms sine cosine algorithm (SCA) and extreme learning machine (ELM) to predict electricity price for a particular duration. The newly developed network model is trained and tested with real-time Indian electricity price data from the year 2022. The selected annual price data set is divided into three different sets to explore seasonal variations and all the sets are given as the input to the model for training and testing to obtain the effective price forecasting.