Electrical Load-Temperature Forecasting for Residential Load Using CNN Model

The need for efficient energy management, grid stability and cost optimization drives the increasing demand for accurate load forecasting in residential areas. Accurate load forecasting enables electricity to allocate resources effectively, plan for peak demand periods, and ensure reliable and sustainable energy supply. Residential load forecasting is crucial as it represents a significant portion of overall electricity consumption. However, accurate load forecasting in residential areas is difficult because of its intricate and ever-changing nature of electricity usage patterns. In recent times, interest in has been steadily increasing leveraging advanced data science techniques to improve load forecasting accuracy. Traditional load forecasting methods, such as statistical approaches, have limitations in capturing the non-linear and non-stationary patterns present in residential load data. These methods frequently depend on past load patterns and may not effectively account for the influence of external factors that impact electricity consumption. One such significant factor is temperature, as it stringly correlates with residential energy usage. The relationship between load and temperature can differ based on elements like seasonality, climate, and location.