Smart City Gnosys

Smart city article details

Title	Hybrid Cnn-Lstm And Domain Modeling In Climate-Energy Analysis For A Smart Environment
ID_Doc	29722
Authors	Shrestha S.; Buckley B.; Varde A.S.; Cwynar D.
Year	2023
Published	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
DOI	http://dx.doi.org/10.1109/ICTAI59109.2023.00041
Abstract	This study aims to model the effect of local climate on energy generation in power plants in the United States. It considers climate change as well as the ever-growing electricity demand, contributing to greenhouse gas emissions. We propose an integration of AI-based and domain-specific paradigms via a hybrid approach of convolutional neural networks (CNN) & long short-term memory (LSTM) coupled with environmental models. Erstwhile studies focus mainly on specific energy sources, e.g. solar/wind power. There is a need for more comprehensive examination, e.g. temperature effects across various energy generation types at the macro level of states and micro level of power plants. This forms the major focus and novelty of our study. Fathoming the factors driving temperature-energy correlations, and assessing power plant vulnerability to changing climate patterns, are both essential to promote energy infrastructure resilience and sustainability. Our study offers novel insights into the complex interplay of temperature, energy generation, sustainability and related aspects to enable enhanced macro & micro level decision-making in energy sector. It therefore makes contributions to smart environment goals for smart cities and a smart planet. © 2023 IEEE.
Author Keywords	Climate Studies; CNN; Electricity; Energy; LSTM; Predictive Modeling; Smart Cities; Smart Environment

Similar Articles

Id	Similarity	Authors	Title	Published
36015	0.885	Suanpang P.; Jamjuntr P.	Machine Learning Models For Solar Power Generation Forecasting In Microgrid Application Implications For Smart Cities	Sustainability (Switzerland), 16, 14 (2024)
6687	0.883	Rao B.S.; Aparna M.; Raju M.S.N.	Advancing Smart City Energy Management: Very Short-Term Photovoltaic Power Generation Forecasting Using Multi-Scale Long Short-Term Memory Deep Learning	Biomass and Solar-Powered Sustainable Digital Cities (2024)
17813	0.879	Sharma P.K.; Kumar M.V.K.; Ahmad M.W.; Radhika M.	Deep Learning And Multi-Horizontal Solar Energy Forecasting Of Different Weather Conditions In Smart Cities	Sustainable Smart Homes and Buildings with Internet of Things (2024)
7417	0.878	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)
26837	0.877	Helli S.S.; Tanberk S.; Demir O.	Forecasting Energy Consumption Using Deep Learning In Smart Cities	Proceedings - 2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022 (2022)
28891	0.877	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)
38700	0.875	Amalou I.; Mouhni N.; Abdali A.	Multivariate Time Series Prediction By Rnn Architectures For Energy Consumption Forecasting	Energy Reports, 8 (2022)
32373	0.87	Mithul Raaj A.T.; Balaji B.; R R S.A.P.; Naidu R.C.; Rajesh Kumar M.; Ramachandran P.; Rajkumar S.; Kumar V.N.; Aggarwal G.; Siddiqui A.M.	Intelligent Energy Management Across Smart Grids Deploying 6G Iot, Ai, And Blockchain In Sustainable Smart Cities	IoT, 5, 3 (2024)
17962	0.868	Aljohani A.	Deep Learning-Based Optimization Of Energy Utilization In Iot-Enabled Smart Cities: A Pathway To Sustainable Development	Energy Reports, 12 (2024)
11151	0.868	Yang F.; Mao Q.	Auto-Evaluation Model For The Prediction Of Building Energy Consumption That Combines Modified Kalman Filtering And Long Short-Term Memory	Sustainability (Switzerland), 15, 22 (2023)