Abstract
Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.
Highlights
The urgent pursuit of low-carbon economy and advances of wind power technologies are strongly driving the rapid sustainable transition in the energy sector as well as the wind power development across the world [1,2]
This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS)
The input instances are divided into a set of subsets using the K-means clustering to train the adaptive neuro-fuzzy interference system (ANFIS)
Summary
The urgent pursuit of low-carbon economy and advances of wind power technologies are strongly driving the rapid sustainable transition in the energy sector as well as the wind power development across the world [1,2]. A forecasting model combining a support vector machine (SVM) optimized by a genetic algorithm and feature selection based on the phase space reconstruction was presented in [15] for short-term wind speed prediction. Numerical weather prediction (NWP) data (including wind speed, direction, temperature, humidity, atmospheric pressure, etc.) were adopted as the input variables for supervised models. The study in [17] proposed a wind power prediction model based on the composite covariance function considering the joint effects among features of NWP data. The main technical contributions made in this work are summarized as follows: hybrid algorithmic solution is presented which considers both historical generation data and NWP adata, novel hybrid algorithmic solution is presented which features considersusing both historical generation and and selects the optimal combination of input a filter method for data different. The optimal input variables are filtered based on mRMR criterion
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