German country-wide renewable power generation from solar plus wind mined with an optimized data matching algorithm utilizing diverse variables

• Published in: Energy systems (Berlin. Periodical) Vol. 11; no. 4; pp. 1003 - 1045
• Main Author: Wood, David A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020; Springer Nature B.V
• Subjects: Algorithms; Alternative energy sources; Data analysis; Data mining; Datasets; Economics and Management; Electric power generation; Electricity pricing; Energy; Energy Policy; Energy Systems; Machine learning; Matching; Operations Research/Decision Theory; Optimization; Original Paper; Outliers (statistics); Renewable energy; Solar power generation; Weather; Wind power; Wind power generation; Machine learning for transparent data mining; Prediction outlier analysis; Combining weather/environmental/market variables; Country-level solar and wind power generation; Data filtering
• ISSN: 1868-3967; 1868-3975
• DOI: 10.1007/s12667-019-00347-x

Country-wide, hourly-averaged solar plus wind power generation (MW) data (8784 data records) published for Germany in 2016 is compiled to include ten influential variables related weather, ground-surface environmental and a specifically calculated day-head electricity price index. The transparent open box (TOB) learning network, a recently developed optimized nearest neighbour, data matching, prediction algorithm, accurately predicts MW and facilitates data mining for this historical dataset. The TOB analysis results in MW prediction outliers for about 1.5% of the data records. These outliers are revealed via TOB analysis to be related to uncommon conditions occurring on a few specific days typical over hourly sequences involving rapid change in weather-related conditions. Such outliers are readily identified and explained individually by the TOB algorithm’s data mining capabilities. A slightly filtered dataset (excluding 129 identified outliers) improves TOB’s prediction accuracy. The TOB algorithm facilitates accurate predictions and detailed evaluation over a range of historical temporal scales on a country-wide basis that could also be applied to regional spatial predictions. These attributes of the TOB method are conducive with it eventually being incorporated into forward-looking renewable forecasting frameworks.