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dc.contributor.authorHrnjica, Bahrudin
dc.contributor.authorDanandeh Mehr, Ali
dc.contributor.editorAl-Turjman, Fadi
dc.date.accessioned2019-09-25T07:22:14Z
dc.date.available2019-09-25T07:22:14Z
dc.date.issued2019
dc.identifier.citationHrnjica, B. & Danandeh Mehr, A. (2019). Energy demand forecasting using deep learning. Al-Turjman, F. (Ed.), Smart Cities Performability, Cognition, & Security. Berlin: Springer.en_US
dc.identifier.isbn9783030147174
dc.identifier.urihttps://doi.org/10.1007/978-3-030-14718-1_4
dc.identifier.urihttp://hdl.handle.net/20.500.12566/63
dc.description.abstractOur cities face non-stop growth in population and infrastructures and require more energy every day. Energy management is the key success for the smart cities concept since electricity is one of the essential resources which has no alternatives. The basic role of the smart energy concept is to optimize the consumption and demand in a smart way in order to decrease the energy costs and increase efficiency. Among the variety of benefits, the smart energy concept mainly enhances the quality of life of the inhabitants of the cities as well as making the environment cleaner. One of the approaches for the smart energy concept is to develop prediction models using machine learning, ML algorithms in order to forecast energy demand, especially for daily and weekly periods. The upcoming chapter describes thoroughly what is behind the deep learning concept as a subset of ML and how neural networks can be applied for developing energy prediction models. A specialized version of the recurrent neural network (RNN), e.g., long short-term memory (LSTM), is described in detail. In addition, the chapter tries to answer the question as to why the LSTM is a state-of-the-art ML algorithm in time series modeling today. To this end, we introduce ANNdotNET, which provides a user-friendly ML framework with capability of importing data from the smart grids of a smart city. By design, the ANNdotNET is a cloud solution which can be connected by other Internet of Things, IoT, devices for data collecting, feeding, and providing efficient models to energy managers in a bigger smart city cloud solution. As an example, the chapter provides the evolution of daily and weekly energy demand models for Nicosia, the capital of Northern Cyprus. Currently, energy demand predictions for the city are not as efficient as expected. Therefore, the results of this chapter can be used as efficient alternatives for IoT-based energy prediction models in any smart city.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherSpringer Nature Switzerland AGen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleEnergy demand forecasting using deep learningen_US
dc.typeinfo:eu-repo/semantics/bookParten_US
dc.relation.publicationcategoryInternational publicationen_US
dc.identifier.startpage71
dc.identifier.endpage104
dc.contributor.orcid0000-0003-2769-106X [Danandeh Mehr, Ali]
dc.contributor.abuauthorDanandeh Mehr, Ali
dc.identifier.none104


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