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

Abstract

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.

Optimal Power Flow Using Gbest‐Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

“…The core idea of CS is inspired by the breeding parasitic characteristics of cuckoo and combined with the Lévy flights behavior. For establishing the mathematic model of CS algorithm, we mainly use three idealized assumptions: (i) every cuckoo can only lay one egg in a randomly chosen nest for one time; (ii) the best nests with better eggs will be retained to next generation; (iii) the number of host nests are invariant during the whole search process [28]. In CS algorithm, a nest is regarded as a candidate solution.…”

Load Prediction of Microgrid Optimal Operation Based on Improved Algorithm in Machine Learning

“…Chang et al [6] proposed a combined prediction model optimized by particle swarm optimization algorithm, compared it with a single model, and verified its reliability in power load prediction. Jiani et al [7] designed a hybrid prediction model combining empirical mode decomposition, cuckoo search algorithm and wavelet neural network, carried out experiments on halfhour power load in New South Wales, Australia, and found that the prediction results were very similar to the actual results. Chitzaz et al [8] trained the self recursive wavelet neural network through Levenberg Marquardt (LM) learning algorithm and verified the effectiveness of the method through experiments.…”

Wind Speed Forecasting System Based on the Variational Mode Decomposition Strategy and Immune Selection Multi-Objective Dragonfly Optimization Algorithm

“…Ma & Liu applied the kernel function method to the grey model, proposed a new timeseries forecasting model, and demonstrated that this method outperforms the traditional grey model in practical applications [32]. Heng et al proposed a hybrid prediction model that is based on empirical mode decomposition (EMD) and the cuckoo search algorithm, which is used to obtain more accurate results for power load forecasting [33]. Wang et al proposed another hybrid prediction method for wind speed prediction that uses the decomposition method, BPNN and the genetic algorithm (GA) [34].…”