Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

“…Yuan et al [22] T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence Rudin et al [23] Machine Learning for the New York City Power Grid Jurado et al [24] Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques Pérez-Chacón et al [25] Big data analytics for discovering electricity consumption patterns in smart cities Peña et al [26] Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach Liu et al [27] A machine learning-based method for the large-scale evaluation of the qualities of the urban environment Muhammed et al [28] UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities Massana et al [29] Identifying services for short-term load forecasting using data driven models in a Smart city platform Wang et al [30] Identification of key energy efficiency drivers through global city benchmarking: a data driven approach Abbasi and El Hanandeh [31] Forecasting municipal solid waste generation using artificial intelligence modelling approaches Badii et al [32] Predicting Available Parking Slots on Critical and Regular Services by Exploiting a Range of Open Data Madu et al [33] Urban sustainability management: A deep learning perspective Gomede et al [34] Application of Computational Intelligence to Improve Education in Smart Cities. Cramer et al [35] An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives You and Yang [36] Urban expansion in 30 megacities of China: categorizing the driving force profiles to inform the urbanization policy Nagy and Simon [37] Survey on traffic prediction in smart cities Belhajem et al [38] Improving Vehicle Localization in a Smart City with Low Cost Sensor Networks and Support Vector Machines Fernández-Ares et al [39] Studying real traffic and mobility scenarios for a Smart City using a new monitoring and tracking system Belhajem et al [40] Improving low cost sensor based vehicle positioning with Machine Learning Gopalakrishnan [41] Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review Khan et al [42] Smart City and Smart Tourism: A Case of Dubai Idowu et al [43] Applied machine learning: Forecasting heat load in district heating system Bellini et al [44] Wi-Fi based...…”

Data-Driven Modelling of Smart Building Ventilation Subsystem

“…Occupancy modelling and estimation is a critical task in smart buildings as the occupancy level and its accurate forecasting directly impact the HVAC conditioning strategy of the building and avoiding wasteful control. Fault and anomaly detection with a rulebased system is described in [5]. The main contributions relate to building automated anomaly detection rules with regard to energy efficiency.…”

In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data

“…Within the highperformance and sustainable building design domain, the use of predictive approaches is usually related to prediction of building energy use and demand [810]; prediction of building occupancy and occupant behaviour [11,12]; and fault detection diagnostics [13,14]. Unsupervised tasks usually complement and target framework development [1517]; discovery of patterns in occupant behaviour for improvement of operational performance [18]; and extraction of energy use patterns [19,20].…”