Locational Marginal Electricity Price Forecasting-Based Self-Attention Mechanism and Simulated Annealing Optimizer using Big Data

Effective short-term Locational Marginal Price Forecasting (LMPF) is difficult in view of the high sensibility of the electricity price in deregulated markets. This paper proposes an accurate forecasting algorithm for LMPF using the latest breakthroughs in deep learning. Specifically, the proposed strategy is composed of a Hybrid Feature Selector (HFS), hyperparameter tuning using Simulated Annealing (SA)-based multi-objective optimization algorithm, and self-Attention-based Long Short-Term Memory (ALSTM). The proposed HFS includes Extreme Gradient Boosting, Elastic Net, and random forest models to rank the features based on their relevance. The experimental results are compared with multiple benchmark algorithms to demonstrate the robustness and efficiency of the proposed framework. The main contributions of this paper include 1) An efficient model perfectly tailored for LMPF is introduced; 2) The effectiveness superiority of the proposed SA-ALSTM is verified on publicly available electricity market data. Extensive experimental results validate the competitive performance of the proposed SA-ALSTM in terms of score measures.