Short Term Load Forecasting With Time Series Analysis

Che Roslee, Che Remle (2010) Short Term Load Forecasting With Time Series Analysis. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. A large variety of mathematical methods have been developed for load forecasting. Short-term load forecasting plays an important role in electric power system operation and planning. An accurate load forecasting not only reduces the generation cost in a power system, but also provides a good principle of effective operation. In this project, the Autoregressive Integrated Moving Average (ARIMA) of Time-Series model will be applied to the short-term load forecasting for the Peninsular Malaysia load data. ARIMA is a practical forecasting method in the electric shortterm load forecasting fields for linear prediction. The choice of the forecasting model becomes the important factor to improve load forecasting accuracy. The aim of this project is to achieve forecasting error that is equal or less than 1.5% using Minitab and XLSTAT statistical software. The data collected is 7 weeks of half an hourly load data for Peninsular Malaysia.

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