Developed case-based reasoning system for short-term load forecasting
Short term load forecasting (STLF) has an essential role in the operation of electric power systems. A developed case-based reasoning (CBR) system is presented to solve STLF problem with the aid of self-organizing maps (SOM) and fuzzy-rough sets method. CBR is composed of the steps of case representation, indexing, retrieval, and adaptation, and the key idea in CBR involves the use of already existing knowledge about objects or situations to predict aspects of similar objects. SOM are trained as a cluster tool in order to organize the old cases with the purpose of speeding up the CBR process. Fuzzy-rough sets method further extends the rough set concept through the use of fuzzy equivalence classes and is presented as a tool to extract principal case attributes. This method uses not only case-specific knowledge of past problems, but also uses additional knowledge derived from the clusters of cases and it provides a new way for selecting proper feature subset and feature weights. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company (HZEPC) in China, and the testing results show that the proposed model is feasible and promising for load forecasting