Smart City Gnosys

Smart city article details

Title	Building Energy Information: Demand And Consumption Prediction With Machine Learning Models For Sustainable And Smart Cities
ID_Doc	13024
Authors	Ardabili S.; Mosavi A.; Várkonyi-Kóczy A.R.
Year	2020
Published	Lecture Notes in Networks and Systems, 101
DOI	http://dx.doi.org/10.1007/978-3-030-36841-8_19
Abstract	Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced. © Springer Nature Switzerland AG 2020.
Author Keywords	And consumption; Big data; Building energy; Deep learning; Energy demand; IoT; Machine learning; Smart cities; Soft computing; Sustainable cities; Sustainable urban development

Similar Articles

Id	Similarity	Authors	Title	Published
54322	0.912	Ardabili S.; Abdolalizadeh L.; Mako C.; Torok B.; Mosavi A.	Systematic Review Of Deep Learning And Machine Learning For Building Energy	Frontiers in Energy Research, 10 (2022)
8877	0.903	Hemlata; Rai M.	An Optimized Demand For Cost And Environment Benefits Towards Smart Residentials Using Iot And Machine Learning	Sustainable Smart Homes and Buildings with Internet of Things (2024)
32444	0.901	Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A.	Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023)
57903	0.9	Daniel H.; Mantha B.R.K.; Soto B.G.D.	Towards A Review Of Building Energy Forecast Models	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
36058	0.898	Wang L.; El-Gohary N.M.	Machine Learning-Based Prediction Of Building Water Consumption For Improving Building Water Efficiency	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
23280	0.894	Huang J.; Algahtani M.; Kaewunruen S.	Energy Forecasting In A Public Building: A Benchmarking Analysis On Long Short-Term Memory (Lstm), Support Vector Regression (Svr), And Extreme Gradient Boosting (Xgboost) Networks	Applied Sciences (Switzerland), 12, 19 (2022)
36081	0.892	Prasad M.S.C.; Dhanalakshmi M.; Mohan M.; Somasundaram B.; Valarmathi R.; Boopathi S.	Machine Learning-Integrated Sustainable Engineering And Energy Systems: Innovations At The Nexus	Harnessing High-Performance Computing and AI for Environmental Sustainability (2024)
47255	0.891	Wu Z.; Chu W.	Sampling Strategy Analysis Of Machine Learning Models For Energy Consumption Prediction	2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021 (2021)
51061	0.889	Arabasy M.; Hussein M.F.; Abu Osba R.; Al Dweik S.	Smart Housing: Integrating Machine Learning In Sustainable Urban Planning, Interior Design, And Development	Asian Journal of Civil Engineering, 26, 1 (2025)
13039	0.887	Chaganti R.; Rustam F.; Daghriri T.; Díez I.D.L.T.; Mazón J.L.V.; Rodríguez C.L.; Ashraf I.	Building Heating And Cooling Load Prediction Using Ensemble Machine Learning Model	Sensors, 22, 19 (2022)