Next Article in Journal
Numerical Study of the Dynamic Response of Heat and Mass Transfer to Operation Mode Switching of a Unitized Regenerative Fuel Cell
Next Article in Special Issue
Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices
Previous Article in Journal
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
Previous Article in Special Issue
Financing Innovations for the Renewable Energy Transition in Europe
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2016, 9(12), 1017;

Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation

Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, 54224 Abu Dhabi, UAE
Author to whom correspondence should be addressed.
Academic Editor: José C. Riquelme
Received: 18 July 2016 / Revised: 24 November 2016 / Accepted: 28 November 2016 / Published: 1 December 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
Full-Text   |   PDF [704 KB, uploaded 2 December 2016]   |  


Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable power generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied solar farm, it is increasingly important to forecast the solar power generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead solar power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the data belonging to the same hour of the day vastly improves the accuracy of the results. Getting more accurate forecasts will help the stakeholders come up with better decisions in resource planning and control when large-scale solar farms are integrated into the power grid. View Full-Text
Keywords: solar power; probabilistic forecasting; regression; machine learning; ensemble models solar power; probabilistic forecasting; regression; machine learning; ensemble models

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ahmed Mohammed, A.; Aung, Z. Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation. Energies 2016, 9, 1017.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top