Forecasting Powerdemand Using Artificial Neural Networks

Electricity load forecasting is very important for the reliable operation of electricitycompanies. Load forecasting helps to make important decisions for generatingelectric power. Load forecasts are extremely important for energy suppliers, financialinstitutions, and other participants in electric energy generation, transmission,distribution, and markets.

Load forecasts can be divided into three categories:

1. Short-term forecasts - usually from one hour to one week
2. Medium forecasts - usually from a week to a year and
3. Long-term forecasts - longer than a year



The forecasts for different time horizons are important for different operations. Short-term load forecasting can help to estimate load flows and to make decisions
that can prevent overloading. Such decisions can be used to the improvement of network reliability and to the reduced occurrences of equipment failures. Long term forecasting helps to know the electric load that want in the future and take necessary actions like building more power stations, for fulfill the needs of people in the country. Artificial Neural networks have been known of for a long time. Today, neural networks are broadly used in science and commercial products. It can be forecasted a daily peak load by using actual load of some similar days of target day. But in that case, there are limitations to improve forecasting accuracy by these methods. Neural networks are essentially non-linear circuits that have the demonstrated capability to do non-linear curve fitting. So ANNs can be applied for the forecasting problems.