Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site

Abstract

Due to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure-correlate-predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference station.

A Review of Classification Problems and Algorithms in Renewable Energy Applications

“…Alternative specific neural or kernel-based classifiers have been tested, such as in [33], where a classification algorithm based on a Bayesian neural network is proposed for long-term wind speed prediction. The long-term wind speed prediction problem is modeled as a classification problem with k classes, corresponding to different (discrete) wind speeds at a given study zone.…”

“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

A New Approach for Bayesian Classifier Learning Structure via K2 Algorithm

“…The addition of another parent only adds unnecessary complexity and increases the number of network parameters. Consequently, other authors [4,5,6,28,8] have proposed the use of more sophisticated methods to overcome these shortcomings, among which are: the use of the K2 algorithms [6,24,25,26,27,28,10,4], the Genetic Search [7,4], the Greedy Search [11,4], the Annealing Simulated [8,4], the Greedy Hill Climber [7,4] and the Repeated Hill Climber [7,4]. Although these algorithms have actually managed to attain performant classifiers, their application has resulted in the frequently and commonly encountered problem of structure-learning computational complexity owing to the increase in the number of descriptive variables.…”

Analysis of the Training Metrics of ANNs and Linear MCP Models Used for Wind Power Density Estimation at A Candidate Site

“…The traditional Measure Correlate Predict (MCP) algorithms [5][6][7] and methods which use Machine Learning [8][9][10][11][12] are the most commonly used techniques to generate the models in the estimation processes. The former generally use a single reference station to generate the model.…”