Abstract
Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. The empirical results demonstrate successful load forecast with a low error rate.
Highlights
Load management is a capability required by load distribution centers and electric utilities
We present a genetic programming approach on the forecasting of long term electrical power consumption of a moderate city in Turkey
As seen from the data, power consumption is in this city is rapidly growing, accurate forecasts can help authorities to make reliable plans
Summary
Load management is a capability required by load distribution centers and electric utilities. Long term load forecasting is guidance for maintenance of electricity installations and construction planning. Power system engineers and electricity generation/distribution utilities attach importance to load forecasting. Load forecasting analyses can be classified as short term, mid term and long term in general. Power system planners require the mid and long term load forecasts to make decisions about planning the mid and long term maintenance, preparing the future investment schedules and developing the generation, transmission and distribution systems. Planning of future investment for the constructions depends on the accuracy of the long term load forecasting considerably [1]. We present a genetic programming approach on the forecasting of long term electrical power consumption of a moderate city in Turkey. The rest of the paper is structured as follows: section 2 summarizes methods of forecasting an unknown function, section 3 gives the details of the application of genetic programming to load forecasting and section 4 summarizes the results and a conclusion is given
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