Different Models to Forecast Electricity Loads

Werner Brockmann, Steffen Kuthe


Forecasting electricity load is an important economic problem. Sometimes suited prediction methods are evaluated by benchmark scenarios, as in case of the world-wide competition within the EUNITE network. Here maximal daily electricity load has to be forecasted over a whole month given data of two years before. Because no formal model exists of how load depends on e.g. temperature or other environmental data, non-formal methods must be used. In this paper different approaches are presented. One is based on simple statistics, whereas other approaches rely on a general model in order to be applicable even for a long-term prediction. It is determined out of the given training data by simple methods. Then this approach is extended mainly by incorporating specific general background knowledge on holidays. Therefore a hybrid crisp-fuzzy system is used which is specified by rules as well as by learning.
PublikationsstatusVeröffentlicht - 2001


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