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A platform for research: civil engineering, architecture and urbanism
Dual-stage attention-based long-short-term memory neural networks for energy demand prediction
Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
Dual-stage attention-based long-short-term memory neural networks for energy demand prediction
Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.
Dual-stage attention-based long-short-term memory neural networks for energy demand prediction
Peng, Jieyang (author) / Kimmig, Andreas (author) / Wang, Jiahai (author) / Liu, Xiufeng (author) / Niu, Zhibin (author) / Ovtcharova, Jivka (author)
2021-01-01
Peng , J , Kimmig , A , Wang , J , Liu , X , Niu , Z & Ovtcharova , J 2021 , ' Dual-stage attention-based long-short-term memory neural networks for energy demand prediction ' , Energy and Buildings , vol. 249 , 111211 . https://doi.org/10.1016/j.enbuild.2021.111211
Article (Journal)
Electronic Resource
English
/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy , Energy consumption pattern recognition , Attention mechanism , /dk/atira/pure/sustainabledevelopmentgoals/sustainable_cities_and_communities , name=SDG 11 - Sustainable Cities and Communities , Long short-term memory network , name=SDG 7 - Affordable and Clean Energy , Energy demand forecasting , Word embedding
DDC:
690
Regional Logistics Demand Prediction: A Long Short-Term Memory Network Method
| DOAJ | 2022