Forecasting of Energy Efficiency in Buildings using Multilayer Perceptron Regressor with Waterwheel Plant Algorithm Hyperparameter Provisionally Accepted

Amal H. Alharbi^1* Doaa Khafaga¹ Ahmed M. Zaki² El-Sayed M. El-kenawy^3* ABDELHAMEED IBRAHIM⁴ Abdelaziz Abdelhamid^5* MARWA M. EID⁶ M El-Said⁷ Nima Khodadadi⁸ Laith Abualigah⁹ Mohammed A. Saeed⁷

• ¹Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia, Saudi Arabia
• ²Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA, United States
• ³Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Egypt
• ⁴Bahrain Polytechnic, Bahrain
• ⁵Department of Computer Science, College of Computing and Information Technology, Shaqra University, Saudi Arabia
• ⁶Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt, Egypt
• ⁷Department of Electrical Engineering, Faculty of Engineering, Mansoura University, Egypt
• ⁸Department of Civil and Architectural Engineering, College of Engineering, University of Miami, United States
• ⁹Artificial Intelligence and Sensing Technologies Research Center, University of Tabuk, Saudi Arabia

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm (WWPA). The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.