Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

RESEARCH ARTICLE Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability Monowar Hossain1*, Saad Mekhilef1*, Firdaus Afifi2, Laith M. Halabi1, Lanre Olatomiwa3, Mehdi Seyedmahmoudian4, Ben Horan5, Alex Stojcevski4 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia, 2 Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia, 3 Department of Electrical and Electronic Engineering, Federal University of Technology, PMB 65, Minna, Nigeria, 4 School of Software and Electrical Engineering, Swinburne University of Technology, Victoria, Australia, 5 School of Engineering, Deakin University, Victoria, Australia * (SM); (MH) Abstract OPEN ACCESS Citation: Hossain M, Mekhilef S, Afifi F, Halabi LM, Olatomiwa L, Seyedmahmoudian M, et al. (2018) Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability. PLoS ONE 13(4): e0193772. https://doi.org/10.1371/journal. pone.0193772 Editor: Xiaosong Hu, Chongqing University, CHINA Received: November 23, 2017 Accepted: February 16, 2018 Published: April 27, 2018 Copyright: © 2018 Hossain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: The authors would like to acknowledge the financial support received from the University of Malaya, Malaysia, through Frontier Research Grant No. FG007-17AFR and Innovative Technology Grant No. RP043B-17AET. Competing interests: The authors have declared that no competing interests exist. In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFISDE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFISPSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations. Introduction The primary energy sources (fossil fuels) will soon be exhausted since they are used at a much higher rate than they are found in the earth’s crust. Moreover, the price of fossil fuels is highly unstable, and it causes huge greenhouse gases (GHG) emissions and environmental pollutions [1, 2]. On the other hand, the wind energy is free, environmentally friendly and clean renewable energy. Consequently, in the fight of global climate change, wind energy is a major solution [3–5]. Globally, installed wind power capacity has reached 432.9GW at the end of 2015 where 63GW was added in 2015 alone [6]. PLOS ONE | https://doi.org/10.1371/journal.pone.0193772 April 27, 2018 1 / 31 Hybrid ANFIS for wind power density prediction Abbreviations: ANFIS, Adaptive neuro-fuzzy inference system; ANN, Artificial neural network; DE, Differential evolution; ELM, Extreme learning machine; GA, Genetic algorithm; GP, Genetic programming; PSO, Particle swarm optimization; SVM, Support vector machine; GMCM, Gaussian mixture copula model; GPR, Gaussian process regression; WT, Wavelet transform; GS, Grid search; GHG, Greenhouse gases; MABE, Mean absolute bias error; MAPE, Mean absolute percentage error; RMSE, Root mean square error; R2, Coefficient of determination; MMD, Malaysian Meteorological Department; WPD, Wind power density. However, wind energy is unstable and subject to intermittent characteristics thus, the accurate prediction of the wind speed and the wind power is a vital part of the successful establishment of the wind energy conversion system [7]. Again, to build a wind farm in any particular location, analysis of wind data, estimation of wind power and energy density are essential [8, 9]. The wind power density (WPD) of a particular location is the measure of the potentiality of wind resources and the chance of extracting wind energy at different wind speed from that location. The knowledge of WPD also helps the designer and investor to understand the performance of wind turbine and to choose the optimal number of a wind turbine with a suitable power rating [10, 11]. The wind power can be computed from several numerical methods [12, 13]. The problem in numerical methods is that they need high computation time. In the recent years, artificial intelligence (AI) techniques have received overwhelming popularity in the field of the wind energy system and other engineering applications as they offer better advantages, including fast computation time, require no knowledge of internal system parameters and compact solutions [14–20]. Generally, wind speed and power prediction are divided into three categories, namely, short-term (30 min to 6 h), medium-term (6h to 24h), and long-term (24h and longer) predictions [7, 21]. Short-term wind prediction In ref. [22], two different short-term wind power prediction methods namely; individual ANN and hybrid strategy based on the physical and statistical methods were developed, where individual ANN and hybrid strategy resulted in 10.67% and 2.01% root mean square error (RMSE) respectively in the prediction. However, the hybrid strategy was 5 times slower than individual ANN. An ANFIS based hybrid model was developed in [23] to predict short-term wind power in Portugal that resulted in MAPE of 5.41%, outperforming five other approaches. The authors used historical wind power data as inputs. In [24], the authors applied both ANN and ANFIS models for hourly wind power prediction for a wind farm located in Southern Italy and their prediction accuracy resulted worse when the prediction horizon was increased. More literature review regarding the application of AI methodologies for the prediction of short-term wind spee (...truncated)