Identifying services for short-term load forecasting using data driven models in a Smart City platform

Abstract

The paper describes an ongoing work to embed several services in a Smart City architecture with the aim of achieving a sustainable city. In particular, the main goal is to identify services required in such framework to define the requirements and features of a reference architecture to support the data-driven methods for energy efficiency monitoring or load prediction. With this object in mind, a use case of short-term load forecasting in non-residential buildings in the University of Girona is provided, in order to practically explain the services embedded in the described general layers architecture. In the work, classic data-driven models for load forecasting in buildings are used as an exampleThis research project has been partially funded through BR-UdGScholarship of the University of Girona granted to Joaquim MassanaRaurich. Work developed with the support of the research groupSITES awarded with distinction by the Generalitat de Catalunya(SGR 2014–2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R) and the European Union’s Horizon2020 Research and Innovation Programme under grant agreementNo 68070

To the matter of the educated development of the construction industry of recreational territories

“…This approach is already implemented through the separate directions of the construction industry development: energy saving, ecological and energy security [10,11,6,17,33], complex safety [34,35,36], sustainable development [7,10,11,[37][38][39], informatization and implementation of the intellectual "smart" systems, etc. The educated construction in many respects unites this directions, and also brings the need of understanding of taking into account the local traditions of culture and the prospects of social development of the region, standard legal limits, stated by local decisions ( Figure 5) providing their considering in the construction (for example, the common design of the territories, free use of its resources, etc.…”

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...…”

“…The study conducted by Massana et al [29] involved the use of autoregressive models adopted to predict a short-term load-forecasting Smart City platform. The goal was to feature services that increased energy efficiency.…”

“…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].…”

Do Smart Citizens Make a Smart City? A Case Study on the Factors Influencing Citizen Behavior Using Lapor Sleman Online-Based

“…Several studies have been conducted to have a more holistic approach in the study of smart city. A smart city brings together technology, government and society to achieve smart economy, smart environment, smart people, smart living, and smart governance (Ahvenniemi et al 2017); (Cocchia, 2014); (Travis, 2017);(Aelenei et al 2016);(Garcia-Ayllon and Miralles, 2015); (Massana et al 2017);(Holler et al 2014);(Allwinkle and Cruickshank, 2011).…”