| Title | A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks |
|---|---|
| ID_Doc | 40350 |
| Authors | Carrera, B; Kim, K |
| Title | A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks |
| Year | 2023 |
| Published | Energies, 16, 22 |
| Abstract | Currently, a smart city should ideally be environmentally friendly and sustainable, and energy management is one method to monitor sustainable use. This research project investigates the potential for a "smart city" to improve energy management by enabling the adoption of various types of intelligent technology to improve the energy sustainability of a city's infrastructure and operational efficiency. In addition, the South Korean smart city region of Songdo serves as the inspiration for this case study. In the first module of the proposed framework, we place a strong emphasis on the data capabilities necessary to generate energy statistics for each of the numerous structures. In the second phase of the procedure, we employ the collected data to conduct a data analysis of the energy behavior within the microcities, from which we derive characteristics. In the third module, we construct baseline regressors to assess the proposed model's varying degrees of efficacy. Finally, we present a method for building an energy prediction model using a deep learning regression model to solve the problem of 48-hour-ahead energy consumption forecasting. The recommended model is preferable to other models in terms of R2, MAE, and RMSE, according to the study's findings. |
| https://www.mdpi.com/1996-1073/16/22/7508/pdf?version=1699577089 |
Similar Articles
| ID | Score | Article |
|---|---|---|
41222
|
Garcia, FCC; Macabebe, EQB Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks(2017) | |
| 43811
|
Chen, CY; Chai, KK; Lau, E AI-Assisted approach for building energy and carbon footprint modeling(2021) | |
| 44605
|
Yang, F; Mao, Q Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory(2023)Sustainability, 15, 22 | |
| 36672
|
Jindal, A; Aujla, GS; Kumar, N; Prodan, R; Obaidat, MS DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR(2018) | |
| 40672
|
Häring, T; Ahmadiahangar, R; Rosin, A; Korotko, T; Biechl, H Accuracy Analysis of Selected Time Series and Machine Learning Methods for Smart Cities based on Estonian Electricity Consumption Forecast(2020) | |
| 64440
|
Shahid, ZK; Saguna, S; Åhlund, C Forecasting Electricity and District Heating Consumption: A Case Study in Schools in Sweden(2023) | |
| 36938
|
Carrera, B; Peyrard, S; Kim, K Meta-regression framework for energy consumption prediction in a smart city: A case study of Songdo in South Korea(2021) | |
| 67055
|
Xin, Q; Alazab, M; Díaz, VG; Montenegro-Marin, CE; Crespo, RG A deep learning architecture for power management in smart cities(2022) | |
| 39221
|
Shafiq, M; Bhavani, NPG; Ramesh, JVN; Veeresha, RK; Talasila, V; Alfurhood, BS Thermal modeling and Machine learning for optimizing heat transfer in smart city infrastructure balancing energy efficiency and Climate Impact(2024) | |
| 42707
|
Abbas, S; Khan, MA; Falcon-Morales, LE; Rehman, A; Saeed, Y; Zareei, M; Zeb, A; Mohamed, EM Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine(2020) |