Abstract
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.
Highlights
Electric load forecasting plays an essential role in making optimal action plans for decision makers, such as load unit commitment, energy transfer scheduling, contingency planning load shedding, energy generation, load dispatch, power system operation security, hydrothermal coordination, and so on [1]
The parameter combinations of support vector regression (SVR) are eventually determined by the bat algorithm (BA), QBA, Chaotic Quantum Bat Algorithm (CQBA), QTS, CQTS, QPSO, CQPSO, QGA, and CQGA, respectively
Denotes that the SVRCQGA model significantly outperforms the other alternative compared models; * represents that the test has rejected the null hypothesis under α = 0.025; ** represents that the test has rejected the null hypothesis under α = 0.005
Summary
Electric load forecasting plays an essential role in making optimal action plans for decision makers, such as load unit commitment, energy transfer scheduling, contingency planning load shedding, energy generation, load dispatch, power system operation security, hydrothermal coordination, and so on [1]. It is important to look for high accurate forecasting models or to develop novel approaches to receive satisfied load forecasting accuracy, which can help decision makers optimize adjust the electricity price/supply and load plan based on the forecasted results, i.e., improve the electricity system operations more efficient, and reduce system operating risks successfully. In the past few decades, lots of electric load forecasting models have been developed to improve load forecasting accuracy. These forecasting methods include two classical types: traditional statistical models and artificial intelligent models. The traditional statistical models are to be Energies 2017, 10, 2180; doi:10.3390/en10122180 www.mdpi.com/journal/energies
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