[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120775-en":3,"doc-seo-120775-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120775,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Prediction of Energy Consumption of an Administrative Building using Machine Learning and Statistical Methods","Energy management is critical amid current energy challenges, especially in the building sector that contributes a significant share of global energy use. This study compares machine learning models with SARIMA models to predict heating energy demand in an administrative building in Chefchaouen, Morocco. Inputs include external and internal temperatures, solar radiation, and time, while TRNSYS simulations generate the dataset for training and validation. Model performance is evaluated with nRMSE, MAE, and correlation coefficient R, showing strong accuracy; the ANN achieves the best results (R=0.97, nRMSE=12.60%, MAE=0.19 kWh).","Prediction of Energy Consumption of an Administrative Building using Machine Learning and Statistical Methods  \nMeryem El Alaoui 1*, Laila Ouazzani Chahidi 2, 3, Mohammed Rougui 1,  \nAbdeghafour Lemrani 1 , Abdellah Mechaqrane 2  \n1 LGCE, Civil Engineering and Environment Laboratory, High School of Technology (EST)-Sale, Mohammed V University, PO. Box  \n227, Rabat Sale, Morocco.  \n2 SIGER, Intelligent Systems, Georesources and Renewable Energies Laboratory, Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, PO. Box 2202, Fez, Morocco.  \n3 LISAC, Computer Science, Signals, Automation and Cognitivism Laboratory, Faculty of Sciences DharMehraz, Sidi Mohamed Ben Abdellah University, PO. Box 1796 Atlas, 30003, Fez, Morocco.  \nReceived 20 February 2023; Revised 23 April 2023; Accepted 27 April 2023; Published 01 May 2023  \nAbstract  \nEnergy management is now essential in light of the current energy issues, particularly in the building industry, which accounts for a sizable amount of global energy use. Predicting energy consumption is of great interest in developing an effective energy management strategy. This study aims to prove the outperformance of machine learning models over SARIMA models in predicting heating energy usage in an administrative building in Chefchaouen City, Morocco. It also highlights the effectiveness of SARIMA models in predicting energy with limited data size in the training phase. The prediction is carried out using machine learning (artificial neural networks, bagging trees, boosting trees, and support vector machines) and statistical methods (14 SARIMA models) . To build the models, external temperature, internal temperature, solar radiation, and the factor of time are selected as model inputs. Building energy simulation is conducted in the TRNSYS environment to generate a database for the training and validation of the models. The models' performances are compared based on three statistical indicators: normalized root mean square error (nRMSE), mean average error (MAE), and correlation coefficient (R) . The results show that all studied models have good accuracy, with a correlation coefficient of 0.90 \u003C R \u003C 0.97. The artificial neural network outperforms all other models (R=0.97, nRMSE=12.60%, MAE= 0.19 kWh) . Although machine learning methods, in general terms, seemingly outperform statistical methods, it is worth noting that  \nSARIMA models reached good prediction accuracy without requiring too much data in the training phase. Keywords: Energy Management; Tertiary Sector; Energy Prediction; Machine Learning; Statistical Methods.  \n1. Introduction  \nThe building sector is one of the most significant energy-consuming sectors in Morocco, representing 33% of definite energy utilization and keeping solid development in yearly energy utilization [1] . Integrating intelligent energy management strategies in this sector is among the switches that could assist with meeting the Kingdom's energy challenges and accomplishing its environmental change targets. Energy prediction in buildings is, therefore, essential for intelligent management. Real-time monitoring and decision-making are made possible by approaches based on artificial intelligence (AI), which can be particularly useful for reducing energy consumption in this sector. Substantial research has been carried out on this objective. Moreover, different machine learning methods can be used to predict building energy use.  \n* [Corresponding author: meryem.elalaoui0@gmail.com](Corresponding author: meryem.elalaoui0@gmail.com)  \n [http://dx.doi.org/10.28991/CEJ-2023-09-05-01](http://dx.doi.org/10.28991/CEJ-2023-09-05-01)  \n© 2023 by the authors. Licensee C.E.J, Tehran, Iran. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nArtificial neural networks (ANN) are am","cbCaiizTsh5kmgdB","https://ap.wps.com/l/cbCaiizTsh5kmgdB","pdf",2347646,1,16,"English","en",105,"# Abstract\n# 1. Introduction\n# Energy Prediction Methods and Model Inputs\n# Model Training, Validation, and Performance Metrics\n# Results and Comparative Findings","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To compare machine learning models with SARIMA models for predicting heating energy consumption in an administrative building in Chefchaouen, Morocco.\"},{\"question\":\"Which variables are used as inputs to the predictive models?\",\"answer\":\"The models use external temperature, internal temperature, solar radiation, and a time factor as input features.\"},{\"question\":\"How are the model performances evaluated?\",\"answer\":\"Performance is compared using normalized root mean square error (nRMSE), mean average error (MAE), and the correlation coefficient (R).\"},{\"question\":\"Which approach performed best and what accuracy does it achieve?\",\"answer\":\"The artificial neural network (ANN) outperforms the other models, reaching R=0.97 with nRMSE=12.60% and MAE=0.19 kWh.\"}]","Prediction of Energy Consumption of an Administrative Building using Machine Learning and Statistical Methods | 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is the main goal of this study?","Question",{"text":75,"@type":76},"To compare machine learning models with SARIMA models for predicting heating energy consumption in an administrative building in Chefchaouen, Morocco.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which variables are used as inputs to the predictive models?",{"text":80,"@type":76},"The models use external temperature, internal temperature, solar radiation, and a time factor as input features.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the model performances evaluated?",{"text":84,"@type":76},"Performance is compared using normalized root mean square error (nRMSE), mean average error (MAE), and the correlation coefficient (R).",{"name":86,"@type":73,"acceptedAnswer":87},"Which approach performed best and what accuracy does it achieve?",{"text":88,"@type":76},"The artificial neural network (ANN) outperforms the other models, reaching R=0.97 with nRMSE=12.60% and MAE=0.19 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