Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods

Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly...

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Bibliographic Details
Published in:Energies (Basel) Vol. 16; no. 3; p. 1495
Main Authors: Polo, Jesús, Martín-Chivelet, Nuria, Alonso-Abella, Miguel, Sanz-Saiz, Carlos, Cuenca, José, de la Cruz, Marina
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2023
Subjects:
Accuracy
Algorithms
Arrays
BIPV
Data mining
Data science
Decision trees
Forecasting
gradient boosting algorithms
Machine learning
Methods
Neural networks
Photovoltaic power generation
Power
PV power forecasting
Solar energy
Support vector machines
Time series
Spain
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondly, probabilistic forecasting was performed with XGBoost combined with the Bootstrap method. The results of this paper show the capabilities of Random Forest and gradient boosting algorithms, such as XGBoost, to work as regressors in time series forecasting of BIPV power. Mean absolute error in the deterministic forecast, using the most influencing exogenous variables, were around 40% and close below 30% for the south and east array, respectively.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en16031495