Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction

被引:2
|
作者
Sauter, Evan [1 ]
Mughal, Maqsood [1 ]
Zhang, Ziming [1 ]
机构
[1] Worcester Polytech Inst, Dept Elect & Comp Engn, 100 Inst Rd, Worcester, MA 01609 USA
关键词
machine learning; deep learning; photovoltaic generation forecasting; spatiotemporal regression;
D O I
10.3390/en16134908
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult by their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatiotemporal projections. The compiled dataset is unique given that it consists of weather and output power data of PVs located at five cities spanning 3 and 6 years in length. Gated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively.
引用
收藏
页数:26
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