Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms

Zhilong Wang, Chen Wang and Jie Wu
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Zhilong Wang: Department of Basic Courses, Lanzhou Polytechnic College, Lanzhou 730050, China
Chen Wang: School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
Jie Wu: School of Mathematics and Computer Science, Northwest University for Nationalities, Lanzhou 730030, China

Sustainability, 2016, vol. 8, issue 11, 1-32

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.

Keywords: wind energy assessment and forecasting; data pre-processing; swarm intelligent optimization; neural network; error evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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