Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

Abstract

Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon.

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

“…To quantify the models' validity, the statistical estimation analysis was obtained for the three cases: RMSE-the root mean square error (5), in which the results are shown as a value between 0 and 1 (0 being 0 attenuation and 1 being 100% attenuation), and where N is the total number of estimations; nRMSE-the normalized mean square error (6), measured in percent (%); MBE-the mean deviation (7), with the same unit as the RMSE; and nMBE-given by (8), expressed in (%) and the dimensionless correlation coefficient Equation (2). Table 1 displays the digitized data (DD) combinations of M1, carried out randomly, that had the highest r value from which the best combination was chosen; this was based on the behavior of the digitized channels in performing the linear regression training with 2400 data points, and then validating the model for M2 with the remaining 800 data points.…”

“…G. Reikard calculated the solar irradiance over time horizons of 60, 30, 15, and 5 min, implementing Autoregressive Integrated Moving Average (ARIMA) with errors between 20% and 90% [6]. Solar irradiance forecasting applied to photovoltaic energy production was implemented using the Smart Persistence algorithm in Machine Learning techniques, achieving an nRMSE of 25% on the best panels over short horizons, and 33% over a 6 h horizon [7].An analysis of energy forecasting in solar-tower plants combining a short-term solar irradiation forecasting scheme with a solar-tower plant model, the System Advisor Model (SAM), was used to simulate the behavior of the Gemasolar and Crescent Dunes plants. The findings showed that the best results appeared for the 90-min horizon, where the annual forecasting energy yield for Gemasolar was 97.34 GWh year while for Crescent Dunes it was 392.57 GWh year [8].…”

“…G. Reikard calculated the solar irradiance over time horizons of 60, 30, 15, and 5 min, implementing Autoregressive Integrated Moving Average (ARIMA) with errors between 20% and 90% [6]. Solar irradiance forecasting applied to photovoltaic energy production was implemented using the Smart Persistence algorithm in Machine Learning techniques, achieving an nRMSE of 25% on the best panels over short horizons, and 33% over a 6 h horizon [7].…”

Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method

“…Relevant literature on load and generation forecasting is quite heterogeneous; this is highlighted by the comparative dissertations in reviews and surveys [8,9], clearly showing that no method outperforms the others in every aspect. Major efforts have been devoted to point prediction, for which researchers and practitioners often individuate Artificial Neural Networks (ANN) [10,11], K-Nearest Neighbors (KNN) [12], support vector regression [13], Random Forests (RF) [14], and multiple linear regression models [15] as the best solutions.…”

“…Pinball Score values averaged over the tasks[11][12][13][14][15]. Bold values highlight the best results for each zone.…”