A Range of Methods for Electricity Consumption Forecasting
Seminar Room 1, Newton Institute
AbstractFor Electricité de France the forecast of electricity consumption is a fundamental problem which has been studied for the last twenty years. It is necessary to be able to provide customers and at the same time, optimize the production at different horizons of time. Results of operating models that use non linear regression or ARMAX methods are satisfying with a current accuracy of 1.5% for the forecast of the following day. But, they have to be continually fitted to be adapted to some very difficult periods of time and to the change of consumption. For a few years, due to the new competitive environment, the electrical load curve has become less regular. Its shape and level which depended essentially on climatic exogenous variables has become more affected by economical and ecological variables. The data is not always available and the time series used are often short. So, we have tried to apply the following alternative methods to answer to problems like adaptivity, nonstationarity, parsimony, lack of data, necessity of forecast interval. In this presentation we will display the operating models and those different classes of models which we applied to electrical consumption forecast. For each model we will present the method used, we will show some practical results and we will discuss the benefits and drawbacks of it.(adaptive Kalmann, GAM, combining algoritms, KWF, Bayesian Methods, ..)
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