Energy Consumption Forecasting through Data Mining

Abstract

Forecasting is vital for effective energy management systems to project future energy requirements and establish demand-supply equilibrium. In this study, we propose to evaluate the effectiveness of a new weather-free forecasting model. The model is created using advanced data mining techniques on an extensive database that contains relevant historical power production data. The motivation behind developing a weather-free model stems from the challenges associated with obtaining reliable weather data in specific scenarios and the desire to reduce computational complexity. By eliminating the reliance on weather data, our model offers a promising alternative approach to energy forecasting, potentially enhancing accuracy and efficiency. In this work, our objectives are twofold. Firstly, we aim to evaluate the interplay between anomaly detection techniques and forecasting model accuracy. Anomalies in energy consumption patterns can significantly impact forecasting accuracy, and thus, understanding their detection and management is crucial. Secondly, we seek to determine the optimal performance metric among three defined metrics explicitly tailored for this application. By addressing these objectives, we aim to contribute to advancing energy forecasting methodologies, providing valuable insights for practical implementation in energy management systems. Our findings have the potential to inform decision-making processes and optimize resource allocation in energy distribution networks, ultimately leading to more sustainable and resilient energy infrastructures.