Identification of key energy efficiency drivers through global city benchmarking: A data driven 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...…”

“…A study conducted by Wang et al [30] identifies, through a systematic study, the main drivers of urban energy efficiency. Data was collected through the Global Power City Index (GPCI) report, amongst other sources, from twenty-five cities located on three continents: Asia, Europe and America.…”

“…The technique of predictive analytics was found to be the most commonly used in the studies (RQ1) [23][24][25][29][30][31]35,37,43,45,47,51,55,57]. Predictions are generally used because they are employed in a variety of situations due to their high predictive accuracy and good interpretation [62].…”

Análise envoltória de dados para a gestão energética em instituições de ensino superior multicampi

“…Independentemente do modelo utilizado, os índices criados pela DEA representam a distância de cada uma das DMU à respectiva curva de eficiência, de modo que para atingir essa fronteira de eficiência, três soluções são possíveis: manter constantes as entradas e aumentar as saídas (DEA orientada a outputs) ou manter constantes as saídas e diminuir as entradas (DEA orientada a inputs), ou, simultaneamente, diminuir as entradas e aumentar as saídas (melhor opção), permitindo o contínuo processo de aprimoramento (WANG et al, 2017). A DEA fornece, portanto, indicações do que melhorar para atingir melhor grau de eficiência em uma análise cíclica, porque pode ser repetida em ciclos de medição subsequentes.…”

Benchmarking South East European Cities with the Sustainable Development of Energy, Water and Environment Systems Index

“…Other studies focused largely on one domain of sustainable development. Wang et al [74] compared the energy efficiency of 25 cities with an impact on global economic activity. The authors found that the European cities in the sample were more efficient than those in North America and Asia.…”