Developing a Mathematical Model Based on Weather Parameters to Predict the Daily Demand for Electricity

The System Control Centre (SCC) of the Ceylon Electricity Board (CEB) in Sri Lanka, conducts short term (hour ahead, day ahead) and medium term (up to three years) demand forecasting based on historical demands, seasonal patterns, time of the day and regional sales forecasts. However, there are no measures taken to include the influence of weather conditions in this forecasting. Temperature and humidity have become increasingly dominant determinants of the electricity demand with the increased use of space cooling equipment in commercial and household sectors.

In this paper, a methodology is presented to develop a linear model to predict the daily electricity demand based on weather parameters which uses historical hourly demand data and meteorological data of four consecutive years. Meteorological parameters (temperature, relative humidity, wind speed and wind direction) are taken as independent parameters while the hourly demand is taken as the dependent parameter. Correction factors are used to include the effect of the yearly demand growth for improved correlation. Each electricity demand data point is multiplied by this correction factor based on the average demand growth (yearly) and the time of the day.

The prediction model consists of 72 independent equations (24 representing a weekday, 24 representing Saturday and 24 representing Sunday). Correction factors are calculated for the calendar holidays, which have a major influence on the electricity demand. Model validation is done for historical weather data as well as for weather forecast data.