Modified artificial neural network based on developed snake optimization algorithm for short-term price prediction.

Short-term prices prediction is a crucial task for participants in the electricity market, as it enables them to optimize their bidding strategies and mitigate risks. However, the price signal is subject to various factors, including supply, demand, weather conditions, and renewable energy sources, resulting in high volatility and nonlinearity. In this study, a novel approach is introduced that combines Artificial Neural Networks (ANN) with a newly developed Snake Optimization Algorithm (SOA) to forecast short-term price signals in the Nord Pool market. The snake optimization algorithm is utilized to optimize both the structure and weights of the neural network, as well as to select relevant input data based on the similarity of price curves and wind production. To evaluate the effectiveness of the proposed technique, experiments have been conducted using data from two regions of the Nord Pool market, namely DK-1 and SE-1, across different seasons and time horizons. The results demonstrate that the proposed technique surpasses two alternative methods based on Particle Swarm Optimization (PSO) and Genetic Algorithms-based Neural Network (PSOGANN) and Gravitational Search Optimization Algorithm-based Neural Network (GSONN), exhibiting superior accuracy and minimal error rates in short-term price prediction. The results show that the average MAPE index of the proposed technique for the DK-1 region is 3.1292%, which is 32.5% lower than the PSOGA method and 47.1% lower than the GSONN method. For the SE-1 region, the average MAPE index of the proposed technique is 2.7621%, which is 40.4% lower than the PSOGA method and 64.7% lower than the GSONN method. Consequently, the proposed technique holds significant potential as a valuable tool for market participants to enhance their decision-making and planning activities.