Enhancing Short- and Medium-Term Electricity Load and Price Forecasting Using a Hybrid CNN-AM Model

Abstract

By using a unique hybrid model, this study seeks to improve the precision and dependability of short- and medium-term power load and price forecasts in the energy market. The paper tackles the urgent need for sophisticated forecasting tools that can handle the complexity of energy data by combining the ensemble empirical mode decomposition (EEMD) algorithm with a convolutional neural network using an attention mechanism (CNN-AM). By efficiently extracting intrinsic mode functions that are essential for precise predictions, EEMD optimises the decomposition process of raw data. Concurrently, the CNN-AM improves the model's capacity to highlight important aspects of the data, which raises the forecasts' interpretability and accuracy. The model's performance is verified on a variety of datasets, demonstrating its potential for reliable use in practical situations. Future research directions are examined, such as dynamic model updates to adjust to changing market conditions, overall model resilience, and the inclusion of external factors affecting load and price. The ultimate goal of this research is to provide energy industry stakeholders with a reliable forecasting tool that will enable strategic decision-making and well-informed risk management.