Abstract
In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (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, ANFIS-PSO, 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.
Highlights
The primary energy sources will soon be exhausted since they are used at a much higher rate than they are found in the earth’s crust
The wind energy potential assessment is very important for independent power producer and governmental organization to determine how efficiently wind power can be extracted from a certain location
An efficient soft computing technique based on adaptive neuro-fuzzy inference system (ANFIS)-Particle swarm optimization (PSO), ANFIS-Genetic algorithm (GA), ANFIS-Differential evolution (DE) and standalone ANFIS prediction models were developed in this paper to predict long-term average wind power density of four different locations in Malaysia
Summary
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. The price of fossil fuels is highly unstable, and it causes huge greenhouse gases (GHG) emissions and environmental pollutions [1, 2]. The wind energy is free, environmentally friendly and clean renewable energy. In the fight of global climate change, wind energy is a major solution [3,4,5]. Installed wind power capacity has reached 432.9GW at the end of 2015 where 63GW was added in 2015 alone [6].
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