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- W4293497065 abstract "A large (43824 h) country-wide solar power load profile (LP), solar irradiance and meteorological dataset (ten variables) for Germany covering years 2015 to 2019 is compiled and forecast with eight machine learning and deep learning (ML/DL) algorithms. Analysis reveals that system curtailments are likely responsible for some outlying predictions. The adaptive boosting (ADA) and random forest (RF) algorithms outperform convolutional neural networks and other algorithms in supervised prediction and forecasting tasks with the dataset. Once tuned with 2015 to 2018 data, ADA and RF forecast 2019 hourly data with root mean squared error (RMSE) of < 0.03. Similar forecasting accuracy is achieved using smaller datasets of three months of historical data from 2015 to 2018 to forecast hourly LP in each month of 2019. Forecasts for July 2019 are associated with the highest errors (RMSE = 0.41). The transparent open box (TOB) algorithm, because it can reveal details of data matching contributions to each of its forecasts, is used to data mine the July 2019 forecasts and conduct in-depth outlier analysis. It reveals nine extreme outliers that each overestimate LP based on irradiance and meteorological inputs. Analysis suggests that substantial overestimates leading to high RMSE for July 2019 are likely due to system curtailments. It is beneficial to combine less transparent (e.g., ADA and RF) with more transparent (e.g. TOB) ML algorithms to accurately forecast and data mine large solar power data sets. It also indicates that solar LP forecasts cannot always rely on irradiance and meteorological variables in isolation. There is a need to be mindful of system constraints and market conditions when predicting LP on a country-wide basis." @default.
- W4293497065 created "2022-08-29" @default.
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- W4293497065 date "2022-10-01" @default.
- W4293497065 modified "2023-10-05" @default.
- W4293497065 title "Country-wide solar power load profile for Germany 2015 to 2019: The impact of system curtailments on prediction models" @default.
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- W4293497065 doi "https://doi.org/10.1016/j.enconman.2022.116096" @default.
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