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Near-term, national solar capacity factor forecasts aided by trend attributes and artificial intelligence

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Abstract

An attribute technique is applied to forecast countrywide solar capacity. Attributes relate to the prior 12 h of a univariate, hourly time series. The approach avoids uncertainties relating to weather-related variables averaged at the country level. It captures impacts of system curtailments due to abnormal market conditions or grid-offtake limitations. Fifteen attributes relating to each hourly record are input to machine/deep learning (ML/DL) models. 43,824 h of solar capacity factor for Britain from 2015 to 2019 is evaluated. Fifteen ML/DL models are trained with 2015–2018 data with cross-validation. Trained models are then applied to forecast unseen 2019 hourly data. The ML/DL model forecast accuracy is compared with that of ARIMA and regression models. Extreme gradient boosting, random forest and adaptive boosting models outperform ARIMA and regression methods in forecasts for hours t0 to t + 12. Those three ML models are more accurate and faster to execute than six DL models evaluated. Suboptimal convergence and/or overfitting hinder the forecasts of DL models with unseen data. A transparent multi-linear regression model is used to identifying attribute influences on the different time period forecasts. The trend attributes are shown to influence the forecasts for different hours ahead in distinct ways.

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    David A. Wood

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Appendix 1

Appendix 1

Forecasting accuracy performance metrics calculated for this study.


Root mean squared error

$${\text{RMSE}} = \left[ {\frac{1}{n}\sum\limits_{i = 1}^{n} {((X_{i} ) - (Y_{i} ))^{2} } } \right]^{\frac{1}{2}}$$
(A1)

Mean absolute error

$${\text{MAE}} = \frac{1}{n}\sum\limits_{i = 1}^{n} {\left| {X_{i} - Y_{i} } \right|}$$
(A2)

Coefficient of determination

$$R^{2} = \left[ {\frac{{\sum\nolimits_{i = 1}^{n} {(X_{i} - X_{{{\text{mean}}}} )(Y_{i} - Y_{{{\text{mean}}}} )} }}{{\sqrt {\sum\nolimits_{i = 1}^{n} {(X_{i} - X_{{{\text{mean}}}} )^{2} \sum\nolimits_{i = 1}^{n} {(Y_{i} - Y_{{{\text{mean}}}} )^{2} } } } }}} \right]$$
(A3)

where Xi = recorded value for data record i; Yi = forecast value for data record i in the subset being considered; n = total number of data records being forecast.

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Wood, D.A. Near-term, national solar capacity factor forecasts aided by trend attributes and artificial intelligence. Int J Energy Environ Eng 13, 1129–1146 (2022). https://doi.org/10.1007/s40095-022-00488-3

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