Short-Term Load Forecasting by Separating Daily Profiles and Using a Single Fuzzy Model Across the Entire Domain

The problem of energy load forecasting has emerged as an essential area of research for electrical distributors seeking to minimize costs. This problem has a high degree of complexity; therefore, this paper solves the problem of short-term load forecasting for a day ahead using an adaptive fuzzy model, defined across the entire input space in order to share information between different areas. The proposed solution first separates the forecasting of daily load profiles into smaller, simpler subproblems, which are solved separably using a Takagi-Sugeno fuzzy model. This is done in order to solve smaller subproblems better, which brings improved forecasting accuracy after combining the subproblem results. The identification of the model is based on a recursive Gustafson-Kessel clustering and recursive weighted least mean squares, to which a combined membership function is proposed in order to improve domain partitioning. The model was tested on the real data obtained from a large Slovenian energy distribution company, at which the developed model forecast outperformed other methods, especially in the start of the week and the winter.