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Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm), and WNN (Wavelet Neural Network), is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network) models such as BPNN (Back Propagation Neural Network), GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm), and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load