Smart City Gnosys

Smart city article details

Title	Load Profile Prediction In Smart Building Using Data Driven Approaches
ID_Doc	35431
Authors	Revati G.; Palak M.; Suryawanshi U.; Sheikh A.; Bhil S.
Year	2021
Published	Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
DOI	http://dx.doi.org/10.1109/AUPEC52110.2021.9597702
Abstract	With the emergence of the smart grid and smart city trends, smart buildings are gaining attention. Energy management is a key component of a smart building management system, and it necessitates precise forecasting of the building's electrical energy usage profile. In view of this, the paper focuses on the analysis of a data-driven approach for predicting the electricity consumption of a smart building in a model-free environment. For predicting the load profile the dynamic mode decomposition (DMD) and deep learning models such as recurrent neural network (RNN), long short term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) model are considered in this paper. The paper also proposes the development of a hybrid model using the best features of RNN and BiLSTM. A comparative study is carried out for analyzing the performance of different data-driven approaches and for that various test scenarios were conducted to evaluate the prediction accuracy. Finally, from the prediction results, it can be claimed that the deep learning models especially the proposed hybrid model outperform other deep learning methods as well as DMD due to the consideration of hyperparameters. © 2021 IEEE.
Author Keywords	Deep Learning; Dynamic Mode Decomposition; Energy Management; Smart Building

Similar Articles

Id	Similarity	Authors	Title	Published
17913	0.968	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)
49203	0.912	Revati G.; Hozefa J.; Shadab S.; Sheikh A.; Wagh S.R.; Singh N.M.	Smart Building Energy Management: Load Profile Prediction Using Machine Learning	2021 29th Mediterranean Conference on Control and Automation, MED 2021 (2021)
7417	0.899	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)
38700	0.895	Amalou I.; Mouhni N.; Abdali A.	Multivariate Time Series Prediction By Rnn Architectures For Energy Consumption Forecasting	Energy Reports, 8 (2022)
12172	0.895	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	0.894	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)
54322	0.881	Ardabili S.; Abdolalizadeh L.; Mako C.; Torok B.; Mosavi A.	Systematic Review Of Deep Learning And Machine Learning For Building Energy	Frontiers in Energy Research, 10 (2022)
28891	0.875	Akbarzadeh O.; Hamzehei S.; Attar H.; Amer A.; Fasihihour N.; Khosravi M.R.; Solyman A.A.	Heating-Cooling Monitoring And Power Consumption Forecasting Using Lstm For Energy-Efficient Smart Management Of Buildings: A Computational Intelligence Solution For Smart Homes	Tsinghua Science and Technology, 29, 1 (2024)
15041	0.874	Satan A.; Khamzina A.; Toktarbayev D.; Sotsial Z.; Bapiyev I.; Zhakiyev N.	Comparative Lstm And Svm Machine Learning Approaches For Energy Consumption Prediction: Case Study In Akmola	SIST 2022 - 2022 International Conference on Smart Information Systems and Technologies, Proceedings (2022)
42779	0.872	Sunny K.; Sheikh A.; Wagh S.; Singh N.M.	Prediction And Classification Of Temperature Data In Smart Building Using Dynamic Mode Decomposition	2020 28th Mediterranean Conference on Control and Automation, MED 2020 (2020)