Abstract
Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.
Highlights
The building sector consumes about one-fifth of the total energy worldwide
We omitted the results of the information gain to reduce duplication
The DL-based model gave the minimum error with the minimum set of features
Summary
The building sector consumes about one-fifth of the total energy worldwide. The world energy demand for buildings is projected to increase from 81 quadrillion Btu in 2010 to approximately 131 quadrillion Btu by 2040 [1,2,3]. Buildings in the United States (US), including commercial and residential, accounted for about 28% of total US end-use energy consumption in 2019 [4]. The primary energy source, accounted for about. 80% of US energy production in the last decade [5]. The combustion of fossil fuels to generate electricity was reported to be the largest single source of carbon dioxide (CO2 ). It has accounted for about 37% of total CO2 emissions and
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have