Techniques for Accurate Forecasting of Household Energy Consumption

Abstract

Accurate forecasting of household energy consumption is essential for optimizing energy management, reducing costs, and improving sustainability in residential sectors. This paper explores various techniques employed in the prediction of household energy usage, focusing on both traditional and advanced methods. We examine statistical models, machine learning algorithms, and deep learning approaches, emphasizing their effectiveness, accuracy, and scalability. Specifically, we analyze time-series forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, with a focus on their applications in real-world scenarios. Additionally, hybrid models that combine multiple techniques for enhanced forecasting accuracy are discussed. Challenges such as data quality, seasonal variations, and the impact of external factors like weather and socio-economic trends are also addressed. Finally, the paper outlines current trends and future directions in the field, highlighting the potential of AI-driven approaches to achieve more precise and dynamic energy consumption forecasts. The findings aim to provide valuable insights for researchers, policymakers, and industry professionals working toward more efficient energy systems and informed decision-making in the residential energy sector.