Predicting the wind power density based upon extreme learning machine
Introduction
Nowadays, the current trend of fossil fuels consumption as well as environmental problems have further motivated incentives towards developing renewable and clean energy sources. As an appropriate renewable energy source, wind energy is being largely harnessed in various regions around the world to enhance the sustainability and reduce some negative environmental issues raised by excessive exploitation of fossil fuels. Owing to the outstanding nature of wind energy which is free, environmental friendly and inexhaustible, countries are performing tremendous efforts to assign a high priority to wind energy harnessing [1]. In fact, adaptation and utilization of the wind energy has received considerable attention all over the world as an alternative source to meet the energy demand. Wind energy is now viewed as the fastest growing source because its development has been characterized by a remarkable rate and this trend is expected to continue. Although investment on wind energy to generate electricity is the cheapest one among all renewables, inappropriate locations for wind turbines installation eventuates in losing huge amounts of money. Therefore, for providing secure wind energy utilization as well as enhancing the efficiency of wind energy markets, wind resources evaluation particularly in terms of realizing wind speed and power predictions is an imperative task [2]. Nevertheless, since the frequency distribution of wind speed may provide different wind power densities for the same wind speed, the knowledge of wind power density would be further reliable. Wind power density resembles the level of accessible energy at the site for converting to electricity by using wind turbines.
Despite the fact that a vast number of mathematical models have been suggested to simulate the wind energy related parameters, there are still disadvantages of the models such as being very demanding in terms of computation time. As a consequence, during the last decade, a large number of scientists worldwide have applied artificial intelligence and computational intelligence techniques to estimate, simulate and optimize various important elements in the realm of wind energy.
Shamshirband et al. [3] performed an investigation on the probability density functions of wind speed and directions based upon the ANFIS (adaptive neuro-fuzzy inference system). They compared the results of ANFIS technique with four wind distribution approaches of Weibull, Frechet, Gumbel and Joint probability density functions. They concluded that ANFIS can represent the wind speed and direction distribution favorably. Karki et al. [4] developed a probabilistic methodology to determine the wind variability and measure the wind power commitment risk throughout the operation of wind energy conversion systems. Chang [5] employed the PSO (particle swarm optimization) technique to assess the wind energy in Taiwan based upon the Weibull distribution function. He calculated the Weibull parameters, the wind speed distribution as well as many important parameters for wind energy evaluation. The obtained results indicated that the PSO method is viable in applications of wind energy. Ma et al. [6] utilized wind power data for all locations distributed across Ireland to suggest some scenarios for attaining the knowledge of error and fluctuation distribution of predicted short-term wind power. Bigdeli et al. [7] predicted the wind power time series for a wind farm located in Alberta, Canada. They developed some hybrid models by combining the NN (neural network) with ICA (imperialist competitive algorithm), GA (genetic algorithm) and PSO techniques. The achieved results showed that the hybrid NN-ICA outperforms other hybrid models. Mohandes and Rehman [8] applied three approaches: the PSO, the AIM (Abductory Induction Mechanism) and the PER (Persistence) for forecasting the 12-h ahead wind speed in Saudi Arabia. They found close agreement between the predicted wind speed data and the measured ones. Petković et al. [9] applied the ANFIS to control and design the wind generator system with CVT (continuously variable transmission). The ANFIS scheme regulated the CVT ratio to achieve the highest power generation efficiency of the wind turbine with extracting maximum wind energy. Bhaskar and Singh [10] proposed an approach, consisted of two steps, to predict wind power based upon the AWNN (adaptive wavelet neural network) as well as the FFNN (feed-forward neural network).
NN, as a major AI (Artificial Intelligence) approach, has been recently introduced and applied in different engineering fields. This method is capable of solving complex nonlinear problems which are difficult to solve by classic parametric methods. NNs can be trained by several algorithms including the GD (Gradient Descent), GDA (Adaptive Learning Rate Gradient Descent Momentum), GDX (Gradient Descent with Adaptive Learning Rate Back Propagation), OSS (One-Step Secant), SCG (Scaled Conjugate Gradient), CGB (Fletcher–Reeves Conjugate Gradient), CGP (Powell–Beale Conjugate Gradient) and CGF (Polak–Ribiere Conjugate Gradient Methods Gradient Descent) [11].
Nevertheless, the major disadvantage of NNs is its learning time requirement. Huang et al. [12], [13] introduced an algorithm for single layer feed forward NN which is known as ELM (Extreme Learning Machine). The ELM algorithm is able to decrease the required time for training a NN. In fact, it has been proved that by utilizing the ELM, learning becomes very fast and it produces good generalization performance [14]. Accordingly, several researchers have been attracted towards applying ELM to solve the problems in considerable scientific areas [15], [16], [17], [18], [19], [20].
A few studies have also been conducted by applying ELM to the wind energy field. Wu et al. [21] performed an investigation to develop an ELM-based model for estimating wind speed and sensorless control of wind turbine systems. Salcedo-Sanz et al. [22] combined the CRO (coral reefs optimization) with ELM to predict short-term wind speed in a wind farm situated in USA. Wan et al. [23] using ELM proposed a model for short-term probabilistic wind power forecasting in a wind farm of Australia.
Reviewing the literature indicates that there is no specific utilization of ELM in estimating wind power density. Therefore, the aim of this research work is to develop an ELM-based model in order to predict the monthly wind power density. The merit of ELM is verified by comparing its predictions accuracy with SVM (Support Vector Machine), ANN (Artificial Neural Network) and GP (Genetic Programming) successfully employed in wind energy area estimations. The wind power density values are calculated based upon the standard deviation and power density methods of the Weibull function to extract the required data for training and testing the models. Afterwards the performance evaluation is accomplished statistically by providing comparisons between the predicted results and the calculated wind powers using real data.
The ELM is a powerful algorithm with faster learning speed compared with traditional algorithms such as the BP (back-propagation). It also has a better performance too. ELM tries to get the smallest training error and norm of weights.
The organization of the remaining part of this paper is as follows: Section 2 explains the wind data and wind power estimation. Section 3 presents the description of ELM. The comparative results and discussion are brought forward in Section 4. Finally, the conclusions are presented in Section 5.
Section snippets
Wind data
Basically, wind speed is measured at a desired site by utilizing anemometers established in a wind mast. In this study, we utilize 3-h wind speed data for the period of five years. In fact, the used wind speed data for this study have been measured at the elevation of 10 m above the ground level in 3-h interval periods. In the first step of analysis, the 3-h wind speed data were averaged to obtain daily data. As mentioned, the main objective of this study was evaluating the suitability of ELM
ELM (extreme learning machine)
The ELM as a tool of learning algorithm has been introduced for SLFN (single layer feed-forward neural network) architecture [12], [36], [37]. The ELM chooses the input weights randomly and determines the output weights of SLFN analytically. The ELM algorithm has a more favorable capability in the learning speed. This algorithm does not require too much human intervention, and can run much faster than the conventional algorithms. It is capable to determine all the network parameters
Results and discussion
In the current study, an ELM is employed to establish a model for predicting the wind power density. To demonstrate the merit of the ELM approach in estimating the wind power density, its prediction performance is assessed by making comparisons with SVM, ANN and GP. SVM, ANN and GP have been widely used by many researchers in solving various problems [41], [42], [43], [44], [45], [46], [47], [48].
In the first step of analysis, the extracted wind power data from the standard deviation and power
Conclusions
The accurate wind power and speed estimations are of great significance in enhancing the financial efficiency and acceptability of the wind energy harnessing. In this paper, an efficient learning model based upon the ELM (extreme learning machine) was developed to estimate the monthly wind power density. The predictions of the ELM model were compared with those of SVM, ANN and GP approaches to determine the suitability of the established ELM model for wind power density predictions. As input
Acknowledgment
This project was supported by the High Impact Research Grant (UM.C/625/1/HIR/MOHE/FCSIT/15) from the University of Malaya and the Ministry of Science, Technology and Innovation (MOSTI), Malaysia.
References (48)
- et al.
A new strategy for predicting short-term wind speed using soft computing models
Renew Sustain Energy Rev
(2012) - et al.
A review of combined approaches for prediction of short-term wind speed and power
Renew Sustain Energy Rev
(2014) - et al.
Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology
Adv Eng Softw
(2014) Wind energy assessment incorporating particle swarm optimization method
Energy Convers Manage
(2011)- et al.
A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada
Renew Sustain Energy Rev
(2013) - et al.
Short term wind speed estimation in Saudi Arabia
J Wind Eng Ind Aerodyn
(2014) - et al.
Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission
Energy
(2014) - et al.
Bankruptcy prediction using extreme learning machine and financial expertise
Neurocomputing
(2014) - et al.
Online sequential extreme learning machine with kernels for nonstationary time series prediction
Neurocomputing
(2014) - et al.
Mobility prediction in mobile ad hoc networks using extreme learning machines
Proced Comput Sci
(2013)
Fast prediction of protein–protein interaction sites based on extreme learning Machines
Neurocomputing
Extreme learning machine towards dynamic model hypothesis in fish ethology research
Neurocomputing
Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search
Renew Energy
Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system
Neurocomputing
Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization–extreme learning machine approach
Energy Convers Manage
Wind energy feasibility study for city of Shahrbabak in Iran
Renew Sustain Energy Rev
Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran
Energy
Technoeconomic analysis of electricity generation from wind energy in Kutahya, Turkey
Energy
Economics of wind turbine as an energy fuel saver e a case study for remote application in Oman
Energy
Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment
Energy
Comparative analysis on power curve models of wind turbine generator in estimating capacity factor
Energy
Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)
Renew Sustain Energy Rev
Economic feasibility of developing wind turbines in Aligoodarz, Iran
Energy Convers Manage
Assessment of wind power potential for turbine installation in coastal areas of Turkey
Renew Sustain Energy Rev
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