Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model

• Hybridizing the chaotic sequence (by cat mapping function), cloud theory, and seasonal mechanism into a SVR model.
• Employing chaotic sequence and cloud theory to improve the original shortcomings of simulated annealing algorithm to avoid premature convergence.
• Successfully providing significant accurate monthly load demand forecasting.

Support vector regression with chaotic sequence and simulated annealing algorithm in previous forecasting research paper has shown its superiority to effectively avoid trapping into a local optimum. However, the proposed chaotic simulated annealing (CSA) algorithm in previous published literature as well as the original SA algorithm could not realize the mechanism of temperature decreasing continuously. In addition, lots of chaotic sequences adopt Logistic mapping function which is distributed at both ends in the interval [0,1], thus, it could not excellently strengthen the chaotic distribution characteristics. To continue exploring any possible improvements of the proposed CSA and chaotic sequence, this paper employs the innovative cloud theory to be hybridized with CSA to overcome the discrete temperature annealing process, and applies the Cat mapping function to ensure the chaotic distribution characteristics. Furthermore, seasonal mechanism is also proposed to well arrange with the cyclic tendency of electric load, caused by economic activities or climate cyclic nature. This investigation eventually presents a load forecasting model which hybridizes the seasonal support vector regression model and chaotic cloud simulated annealing algorithm (namely SSVRCCSA) to receive more accurate forecasting performance. Experimental results indicate that the proposed SSVRCCSA model yields more accurate forecasting results than other alternatives.