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Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid


In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales, Australian grid, is used in the experimentation for 1-h-ahead forecast with an emphasis on data analysis and preprocessing framework. The correlation analysis is used for the identification and selection of the most influential input variables. The simulation results reveal that the proposed combination technique effectively enhanced the prediction accuracy as compared to the available conventional methods. The prediction of 1-h-ahead load demand is critically important for decision-making response of the modern smart grid system. The acceptable precision of the proposed model concludes that it can be applied in the smart grid to enhance its demand responsiveness and other intelligent features.

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Artificial neural network




Backpropagation neural network


Chaotic genetic algorithm


Chaotic genetic algorithm modified backpropagation


Chaotic genetic algorithm-simulated annealing modified backpropagation


Conventional backpropagation


Demand response


Genetic algorithm modified backpropagation


Information and communications technology


Input variable selection


Mean absolute error


Mean absolute percentage error


Mean square error


Modified backpropagation


Multilayer perceptron


Multilayer perceptron neural network


Neural network


Simulated annealing


Smart grid


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The authors wish to thank Universiti Teknologi PETRONAS for providing the research Grant (Number URIF 0153AA-B13) to conduct this research.

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Correspondence to Badar Islam.

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Islam, B., Baharudin, Z. & Nallagownden, P. Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid. Neural Comput & Applic 28, 877–891 (2017).

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  • Artificial neural network
  • Demand response
  • Smart grid
  • Real-coded genetic algorithm
  • Electrical energy demand prediction
  • Chaotic mapping
  • Simulated annealing