Electricity demand forecasting has attracted the attention of many researchers and power company staff. It still does so because with better forecasting, power companies can approach exact plans with no over- or –under planning. This is reflected as being the right investment in terms of time, money, and performance. In essence a good demand forecast means the right investment plan and therefore, satisfied customers. In reality this is the objective of any business; to be able to estimate the demand as close to reality as possible. The number and extent of demand forecasting methodologies and models developed is tremendous, however, there exists no novel technique that can serve all situations. Basically forecasting models can be divided into statistically based and intelligence-based models. A description of forecasting models helps in identifying the characteristics, features, and strengths of each model. The selection of the most suitable forecasting algorithm is not an easy process. The time frame of the forecast, data availability, the accuracy and cost of the forecast, the application and purpose of the forecast are some of the important parameters in the selection process. A case study of two forecasting models used in Jordan is presented. The discussion of the case study shows that load forecasting in Jordan is based on an intelligence-based model for short term forecasting, and on a combination of traditional statistically-based models for long term forecasting.
A study on electricity demand forecasting based on time series clustering in smart grid
