Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China

Zhao, Pan; Wang, Jiangfeng; Xia, Junrong; Dai, Yiping; Yingxin, Sheng; Ye, Jie

doi:10.1016/j.renene.2011.11.051

Cited by 103 publications

(41 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methodological hierarchical model in this paper is based on GIS-Multicriteria decision analysis structure (MCDA). Integrating GIS with the techniques for decision making creates a powerful tool for solving the problem of selecting optimal wind farm locations [42][43][44].…”

Section: Gis-multi-criteria Model Based On Rough Numbersmentioning

confidence: 99%

“…To determine the weight coefficients of the criteria the BWM was modified using the rough approach. The BWM [41][42][43][44][45] is among the more recent methods. The primary advantages of the BW method suggested by the authors are as follows: (1) Compared with the Analytic Hierarchy Process (AHP) method, which in the literature is most commonly used to determine the weight coefficients [46,47], it requires significantly less pair wise comparison (the AHP method requires n(n − 1)/2 comparison, BWM 2n − 3 comparison); (2) the values of the weight coefficients obtained by the BWM are more reliable because comparison in the BWM is carried out with a higher consistency ratio compared with the AHP method; (3) while for the majority of MCDM models (e.g., AHP) the consistency ratio is a test of whether the comparison of criteria is consistent or not, in the BWM the consistency ratio is used to determine the level of confidence since the outputs from the BWM are always consistent; (4) the BWM for pair wise comparison of criteria uses only integers as opposed to other MCDM methods (e.g., AHP) which also require the use of fractional numbers.…”

Section: Gis-multi-criteria Model Based On Rough Numbersmentioning

confidence: 99%

See 1 more Smart Citation

Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers

et al. 2017

View full text Add to dashboard Cite

This paper presents spatial mathematical model in order to identify sites for the wind farms installment which can have significant support for the planners in the area of strategy and management of wind power use. The suggested model is based on combined use of Geographical Information Systems (GIS) with multi-criteria techniques of Best-Worst method (BWM) and MultiAttributive Ideal-Real Comparative Analysis (MAIRCA). Rough numbers and fuzzy logic are used to exploit uncertainty during data analysis in spatial mathematical model. The model is applied on the case study. Rough BWM model is used to determine weight coefficients of the criteria and rough MAIRCA method is used to rank separated sustainable locations. The implementation of MAIRCA method has shown that the location L3 is the most suitable for the wind farm in the area covered in the case study. Therefore, the suggested spatial mathematical model can be successfully used to identify the potential suitable sites for the wind farms in other areas with similar geographic conditions.

show abstract

Section: Gis-multi-criteria Model Based On Rough Numbersmentioning

confidence: 99%

Section: Gis-multi-criteria Model Based On Rough Numbersmentioning

confidence: 99%

Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers

et al. 2017

View full text Add to dashboard Cite

show abstract

“…These methods included a Kalman filter approach [15], ANN approach [19], bias correction methods and a combination of these. In [16], a WRF model is implemented together with the Kalman filter method for wind speed and wind power forecasting for a wind farm in China. Kalman filter approaches have also been applied in [17] and [18], as post-processing tools for correcting the bias of WRF wind speed predictions, reducing significantly the size of the training set, compared to ANN based methods.…”

Section: Related Workmentioning

confidence: 99%

Characterisation of Large Changes in Wind Power for the Day-Ahead Market Using a Fuzzy Logic Approach

et al. 2014

View full text Add to dashboard Cite

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power output that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented

show abstract

“…2 Advanced prediction techniques are urgently needed to integrate wind energy into the electrical power grid in a manner that benefits both Transmission System Operators (TSOs) and Independent Power Producers (IPPs) [2,3].…”

Section: Introductionmentioning

confidence: 99%

Comparison Study of AI-based Methods in Wind Energy

Jiang¹,

Liu²,

Song³

2016

New Applications of Artificial Intelligence

View full text Add to dashboard Cite

Wind energy forecasting is particularly important for wind farms due to cost-related issues, dispatch planning, and energy market operations. Thus, improving forecasting accuracy becomes an urgent task for researchers in the field of wind energy. However, there is limited research to discuss an overall comparison among various forecasting types, which is a foundation for future works with respect to wind energy prediction because this comparison may reveal whether there is a best model for a specific forecasting type or specific data in this field. For the purpose of laying a strong foundation for wind energy research, this chapter introduces five basic forecasting models, which are Autoregressive Moving Average Model (ARMA), Back-Propagation Neuron Network (BPNN), Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Adaptive Network-Based Fuzzy Inference System (ANFIS) with implement codes before comparing the forecasting effectiveness of five different models in three wind farms based on five forecasting types. Comparison results indicate that each model has great divergent forecasting results in different wind farms and every forecasting type has its own "best model."

show abstract

Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China

Cited by 103 publications

References 26 publications

Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers

Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers

Characterisation of Large Changes in Wind Power for the Day-Ahead Market Using a Fuzzy Logic Approach

Comparison Study of AI-based Methods in Wind Energy

Contact Info

Product

Resources

About