Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities
作者：
Highlights：
• Deep neural networks and tree-partitioning methods have shown the potential in predicting energy consumption and identifying key predictors.
• The architecture of an intelligent system for energy management in public sector is suggested.
• IoT network, Big Data collection, and machine learning methods are used to collect and process data.
• The system provides measurable benefits in the form of increased energy efficiency and reduced cost.
• The architecture could be used in public sector business intelligence systems to support decisions on investments in reconstruction measures.
摘要
•Deep neural networks and tree-partitioning methods have shown the potential in predicting energy consumption and identifying key predictors.•The architecture of an intelligent system for energy management in public sector is suggested.•IoT network, Big Data collection, and machine learning methods are used to collect and process data.•The system provides measurable benefits in the form of increased energy efficiency and reduced cost.•The architecture could be used in public sector business intelligence systems to support decisions on investments in reconstruction measures.
论文关键词：O21,O38,P18,Planning models,Energy efficiency,Machine learning,Public sector,Smart cities
论文评审过程：Received 4 March 2019, Revised 1 January 2020, Accepted 10 January 2020, Available online 29 January 2020, Version of Record 15 March 2021.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2020.102074
