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Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, Salvador 40210-630, Brazil
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Author to whom correspondence should be addressed.
Energies 2019, 12(10), 1891; https://doi.org/10.3390/en12101891 (registering DOI)
Received: 13 January 2019 / Revised: 1 April 2019 / Accepted: 8 April 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Artificial Intelligence for Smart-Grid Applications)
PDF [1475 KB, uploaded 17 May 2019]

Abstract

Load forecasting is of crucial importance for smart grids and the electricity market in
terms of the meeting the demand for and distribution of electrical energy. This research proposes
a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic
algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is
a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output
of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed
to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate
an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models.
In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization
(ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for
optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test
the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy.
Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063,
65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.
Keywords: electric load forecasting; non-dominated sorting genetic algorithm II; multi-layer perceptron; adaptive neuro-fuzzy inference system; meta-heuristic algorithms electric load forecasting; non-dominated sorting genetic algorithm II; multi-layer perceptron; adaptive neuro-fuzzy inference system; meta-heuristic algorithms
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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MDPI and ACS Style

Jadidi, A.; Menezes, R.; de Souza, N.; de Castro Lima, A.C. Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model. Energies 2019, 12, 1891.

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