Smart City Gnosys
Smart city article details
| Title | Energy Demand Forecasting Using Deep Learning |
|---|---|
| ID_Doc | 23190 |
| Authors | Hrnjica B.; Mehr A.D. |
| Year | 2020 |
| Published | EAI/Springer Innovations in Communication and Computing |
| DOI | http://dx.doi.org/10.1007/978-3-030-14718-1_4 |
| Abstract | Our 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. © 2020, Springer Nature Switzerland AG. |
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