TY - JOUR
T1 - A spatio-temporal analysis approach for short-term forecast of wind farm generation
AU - He, Miao
AU - Yang, Lei
AU - Zhang, Junshan
AU - Vittal, Vijay
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014/7
Y1 - 2014/7
N2 - In this paper, short-term forecast of wind farm generation is investigated by applying spatio-temporal analysis to extensive measurement data collected from a large wind farm where multiple classes of wind turbines are installed. Specifically, using the data of the wind turbines' power outputs recorded across two consecutive years, graph-learning based spatio-temporal analysis is carried out to characterize the statistical distribution and quantify the level crossing rate of the wind farm's aggregate power output. Built on these characterizations, finite-state Markov chains are constructed for each epoch of three hours and for each individual month, which accounts for the diurnal non-stationarity and the seasonality of wind farm generation. Short-term distributional forecasts and a point forecast are then derived by using the Markov chains and ramp trend information. The distributional forecast can be utilized to study stochastic unit commitment and economic dispatch problems via a Markovian approach. The developed Markov-chain-based distributional forecasts are compared with existing approaches based on high-order autoregressive models and Markov chains by uniform quantization, and the devised point forecasts are compared with persistence forecasts and high-order autoregressive model-based point forecasts. Numerical test results demonstrate the improved performance of the Markov chains developed by spatio-temporal analysis over existing approaches.
AB - In this paper, short-term forecast of wind farm generation is investigated by applying spatio-temporal analysis to extensive measurement data collected from a large wind farm where multiple classes of wind turbines are installed. Specifically, using the data of the wind turbines' power outputs recorded across two consecutive years, graph-learning based spatio-temporal analysis is carried out to characterize the statistical distribution and quantify the level crossing rate of the wind farm's aggregate power output. Built on these characterizations, finite-state Markov chains are constructed for each epoch of three hours and for each individual month, which accounts for the diurnal non-stationarity and the seasonality of wind farm generation. Short-term distributional forecasts and a point forecast are then derived by using the Markov chains and ramp trend information. The distributional forecast can be utilized to study stochastic unit commitment and economic dispatch problems via a Markovian approach. The developed Markov-chain-based distributional forecasts are compared with existing approaches based on high-order autoregressive models and Markov chains by uniform quantization, and the devised point forecasts are compared with persistence forecasts and high-order autoregressive model-based point forecasts. Numerical test results demonstrate the improved performance of the Markov chains developed by spatio-temporal analysis over existing approaches.
KW - Distributional forecast
KW - Markov chains
KW - graphical learning
KW - point forecast
KW - short-term wind power forecast
KW - spatio-temporal analysis
KW - wind farm
UR - http://www.scopus.com/inward/record.url?scp=84903191929&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2014.2299767
DO - 10.1109/TPWRS.2014.2299767
M3 - Article
AN - SCOPUS:84903191929
VL - 29
SP - 1611
EP - 1622
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 4
M1 - 6727513
ER -