Abstract
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.
Highlights
Electricity demand forecasting plays a vital role in power system planning
With the introduction of electricity based heating, ventilation and air conditioning (HVAC) systems, the relationship between the electricity demand and weather conditions have become strong since changing weather conditions have a clear impact on the operation of HVAC systems [1]
This paper presents a model to predict the daily electricity demand using weather forecast parameters
Summary
Electricity demand forecasting plays a vital role in power system planning. Long-term forecasts are used for infrastructure development while short term demand forecasting facilitates merit order plant dispatch. The SCC carries out yearly energy prediction and day to day demand (daily load curve) prediction. A streamlined method to use meteorological parameters for demand prediction has so far not been implemented in the day to day demand prediction. The CEB has to meet different daily demands depending on changing weather conditions. This type of demand variations cannot be predicted by the existing prediction methods. This paper presents a model to predict the daily electricity demand using weather forecast parameters. The influence of weather on the electricity demand can be included in the demand forecasting process, minimizing prediction errors.
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