Abstract
Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR) model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS) algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence). Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS) model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of electric loads, a seasonal mechanism is combined with the SVRCCS model, namely giving a seasonal SVR with chaotic cuckoo search (SSVRCCS) model, to produce more accurate forecasting performances. The numerical results, tested by using the datasets from the National Electricity Market (NEM, Queensland, Australia) and the New York Independent System Operator (NYISO, NY, USA), show that the proposed SSVRCCS model outperforms other alternative models.
Highlights
Accurate electric load forecasting is important to facilitate the decision-making process for power unit commitment, economic load dispatch, power system operation and security, contingency scheduling, and so on [1,2]
It is clearly indicated that the proposed SVR with chaotic cuckoo search (SVRCCS) model has achieved smaller forecasting performances in terms of the forecasting accuracy indexes, mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE)
Denotes that the seasonal SVR with chaotic cuckoo search (SSVRCCS) model significantly outperforms the other alternative compared models; * represents that the test indicates not to accept the null hypothesis under α = 0.05. ** represents that the test indicates not to accept the null hypothesis under α = 0.025
Summary
Accurate electric load forecasting is important to facilitate the decision-making process for power unit commitment, economic load dispatch, power system operation and security, contingency scheduling, and so on [1,2]. Structural methods to determine the SVR parameters are lacking Hong and his colleagues have pointed out the advanced exploration way by hybridizing chaotic mapping functions with evolutionary algorithms to overcome the embedded premature convergence problem, to select suitable parameter combination, to achieve highly accurate forecasting performances. To continue this valuable exploration, the chaotic cuckoo search algorithm, the CCS algorithm, is proposed to be hybridized with an SVR model to determine an appropriate parameter combination
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