This paper proposes a simplified fuzzy inference model for short-term load forecasting in power systems. The simplified fuzzy model is tuned up with tabu search and supervised learning. The proposed method uses tabu search for optimizing the location and number of the fuzzy membership functions. Tabu search is one of meta-heuristic methods that give better solution in a sense of global optimization. Supervised learning is introduced to give better fuzzy inference results. In the proposed model, selection of an input variable is addressed to give an insight into the accumulative effect of the discomfort index with delay. The proposed model is applied to real data and the effectiveness is demonstrated.
|Number of pages||7|
|Journal||International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications|
|Publication status||Published - 1 Jun 2002|
- Discomfort index
- Load forecasting
- Simplified fuzzy inference
- Tabu search