Applying Scikit-Learn of Machine Learning To Predict Consumed Energy in Al-Khwarizmi College of Engineering, Baghdad, Iraq
DOI:
https://doi.org/10.22153/kej.2024.10.005Abstract
Buildings account for 40% of energy usage worldwide. The physical characteristics of the building, the effectiveness of the heating and cooling systems, the inhabitants' activities, and the sustainability of the building are just a few of the many factors that influence a building's energy consumption. It is quite challenging to estimate the energy requirements of a structure. Estimating the building's energy demand is vital to increase sustainability and develop sustainable energy sources to lower carbon dioxide emissions from the burning of fossil fuels. The energy used in the lecture hall at the University of Baghdad (UOB), located in Baghdad, Iraq's Al-Khwarizmi College of Engineering, is explained in this study. The weather data and the building construction information were collected for a specific period and put into a specific data set. That data was used to find the value of energy consumption in the building using artificial intelligence and data analysis. A Python library called Scikit-learn is used to implement machine learning algorithms. In particular, the Multi-layer Perceptron regressor (MLPRegressor) algorithm was used to predict the consumption. The importance of this work lies in predicting the amount of energy consumed. The outcomes of this work can be used to predict the energy consumed by any building before it is built. The used methodology shows the ability to predict energy performance in educational buildings using previous results and train the model on them, and prediction accuracy depends on the amount of data available for the training in artificial intelligence (AI) steps to give the highest accuracy. The prediction was checked using root-mean-square error (RMSE) and coefficient of determination (R²) and we arrived at 0.16 and 0.97 for RMSE and R², respectively.
(Received 20 July 2023; accepted 2 October 2023; Published 1 March 2024)
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