Short-Term Electricity Demand Forecast Leveraging Google Trends Data Toward Smart Green Energy Supply
摘要
Accurate prediction of short-term power demand is essential for efficient energy management and grid stability. This paper explores the integration of Google Trends data with transformer-based model for enhancing power demand forecasting. We use public concerns derived from Internet data as a complementary driving factor to forecast electricity demand. Our specific case is the UK region, but the models presented in this study will have a broader applicability. Traditional methods rely on historical consumption patterns and weather conditions, but this approach offers improved accuracy and timeliness.