Abstract-Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. In 2001 EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem. In this paper, we discuss in detail how SVM, a new learning technique, is successfully applied to load forecasting. In addition, motivated by the competition results and the approaches by other participants, more experiments and deeper analyses are conducted and presented here. Some important conclusions from the results are that temperature (or other types of climate information) might not be useful in such a midterm load forecasting problem and that the introduction of timeseries concept may improve the forecasting.
In REF , a support vector machine (SVM) was adopted to forecast the daily peak load of the next 31 days.
Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001
Computer Science