Generalized Locally Weighted GMDH for Short Term Load Forecasting

This paper proposes a generalized locally weighted group method of data handling (G-LWGMDH) based on evolutionary algorithm (EA) for short-term load forecasting. The locally weighted group method of data handling (LWGMDH) can be derived by combining GMDH with the local regression method and weighted least squares (WLS) regression. The connectivity configuration in the G-LWGMDH is not limited to adjacent layers, unlike the conventional GMDH. Moreover, each node in the G-LWGMDH network has a different number of inputs and a different polynomial order. The performance of the G-LWGMDH depends on choosing these factors before the network is constructed. Therefore, EA is used in this paper to optimally select these factors. In the proposed method, a new encoding scheme is presented, where each chromosome represents the structure of the whole network. The weighting functions bandwidth, the polynomial order for each node, the number of inputs for each node, and the input variables chosen to each node are encoded as a chromosome. The performance of the proposed method (EA-based G-LWGMDH) is evaluated using two real-world datasets. The results show that the proposed method provides a much better prediction performance in comparison with other methods employing the same data.