Abstract
Accurate quantification and characterization of a wind energy potential assessment and forecasting is significant to optimal wind farm design, evaluation and scheduling. However, wind energy potential assessment and forecasting remain difficult and challenging research topics at present. Traditional wind energy assessment and forecasting models usually ignore the problem of data pre-processing as well as parameter optimization, which leads to low accuracy. Therefore, this paper aims to assess the potential of wind energy and forecast the wind speed in four locations in China based on the data pre-processing technique and swarm intelligent optimization algorithms. In the assessment stage, the cuckoo search (CS) algorithm, ant colony (AC) algorithm, firefly algorithm (FA) and genetic algorithm (GA) are used to estimate the two unknown parameters in the Weibull distribution. Then, the wind energy potential assessment results obtained by three data-preprocessing approaches are compared to recognize the best data-preprocessing approach and process the original wind speed time series. While in the forecasting stage, by considering the pre-processed wind speed time series as the original data, the CS and AC optimization algorithms are adopted to optimize three neural networks, namely, the Elman neural network, back propagation neural network, and wavelet neural network. The comparison results demonstrate that the new proposed wind energy assessment and speed forecasting techniques produce promising assessments and predictions and perform better than the single assessment and forecasting components.
Highlights
As a clean and renewable resource, wind energy is important in energy supply and, through wind turbines, the green wind energy can be converted to electricity
The maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model according to observations by finding the parameter values that maximize the likelihood of making the observations given the parameters
Effective wind energy potential assessment and forecasting for a particular site plays an indispensable role in the design, evaluation and scheduling of wind farms
Summary
As a clean and renewable resource, wind energy is important in energy supply and, through wind turbines, the green wind energy can be converted to electricity. Wind energy assessment should be performed in advance. Wind energy assessment and wind speed forecasting are two challenging research topics at present. Based on different moment constraints, Liu and Chang [1] performed validity analysis of the maximum entropy distribution for wind energy assessment in Taiwan. Nested ensemble Numerical Weather Prediction approach was proposed by Al-Yahyai et al [2] to perform a wind energy assessment over Oman. Wu et al [3] proposed an assessment model based on the Weibull distribution and different particle swarm optimization algorithms as well as differential evolution algorithms to assess the wind energy potential at Inner Mongolia in China. The wind analysis model was adopted by Boudia et al [5] to assess the wind energy of four locations situated in the Algerian Sahara. A GIS-based method was applied by Siyal et al [7] for wind energy assessment in Sweden
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