Smart City Gnosys

Smart city article details

Title	Multivariate Time Series Prediction By Rnn Architectures For Energy Consumption Forecasting
ID_Doc	38700
Authors	Amalou I.; Mouhni N.; Abdali A.
Year	2022
Published	Energy Reports, 8
DOI	http://dx.doi.org/10.1016/j.egyr.2022.07.139
Abstract	Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective, smart grids are a new technology and a new generation of traditional electric networks that provide increased efficiency, dependability, and energy management based on demand optimization. The importance of smart grids can also be seen in the possibility of integrating communication systems for energy demand forecasting, to provide an optimal management of the combination of renewable energies and production centers energy. The authors present a comparative analysis of several deep learning models, notably Recurrent Neural Network (RNN) architectures such as basic RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), in this paper. These architectures are trained and tested on the Smart Grid Smart City (SGSC) project's energy datasets (2010–2014) and assessed using a variety of indicators such as Root Mean Square Error (RMSE), Mean Absolute Error MAE, and R2 scores in order to analyze, compare and ultimately choose the most efficient model. As expected from the literature of RNN architectures, with the lowest value of RMSE error and the highest value of R2 Score among the three architectures, GRU outperformed both of basic RNN and LSTM, this result can be explained by several reasons the most important one is the ability of the GRU model to deal with the vanishing gradient problem and the impact of the number of parameters, used in building such a model, on the same problem. © 2022 The Author(s)
Author Keywords	Deep Learning; Energy forecasting; RNN architectures; Smart grids; Time series data

Similar Articles

Id	Similarity	Authors	Title	Published
48664	0.904	Shaikh A.K.; Nazir A.; Khan I.; Shah A.S.	Short Term Energy Consumption Forecasting Using Neural Basis Expansion Analysis For Interpretable Time Series	Scientific Reports, 12, 1 (2022)
12172	0.904	So D.; Oh J.; Jeon I.; Moon J.; Lee M.; Rho S.	Bigta-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model For Building Energy Management Systems	Systems, 11, 9 (2023)
3739	0.903	Noaman S.A.; Ahmed A.M.S.; Salman A.D.	A Prediction Model Of Power Consumption In Smart City Using Hybrid Deep Learning Algorithm	International Journal on Informatics Visualization, 7, 4 (2023)
23190	0.898	Hrnjica B.; Mehr A.D.	Energy Demand Forecasting Using Deep Learning	EAI/Springer Innovations in Communication and Computing (2020)
42542	0.897	Huang F.; Zhuang S.; Yu Z.	Power Load Prediction Based On An Improved Clock-Work Rnn	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 (2019)
35431	0.895	Revati G.; Palak M.; Suryawanshi U.; Sheikh A.; Bhil S.	Load Profile Prediction In Smart Building Using Data Driven Approaches	Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021 (2021)
17913	0.893	Palak M.; Revati G.; Hossain M.A.; Sheikh A.	Deep Learning Models For Smart Building Load Profile Prediction	Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021 (2021)
38451	0.891	Safari A.; Kharrati H.; Rahimi A.	Multi-Term Electrical Load Forecasting Of Smart Cities Using A New Hybrid Highly Accurate Neural Network-Based Predictive Model	Smart Grids and Sustainable Energy, 9, 1 (2024)
30937	0.891	Alsharekh M.F.; Habib S.; Dewi D.A.; Albattah W.; Islam M.; Albahli S.	Improving The Efficiency Of Multistep Short-Term Electricity Load Forecasting Via R-Cnn With Ml-Lstm	Sensors, 22, 18 (2022)
7417	0.889	Sunder R.; R S.; Paul V.; Punia S.K.; Konduri B.; Nabilal K.V.; Lilhore U.K.; Lohani T.K.; Ghith E.; Tlija M.	An Advanced Hybrid Deep Learning Model For Accurate Energy Load Prediction In Smart Building	Energy Exploration and Exploitation, 42, 6 (2024)