A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A building energy consumption prediction model based on rough set theory and deep learning algorithms
Highlights The combination of rough set theory and deep learning algorithms is analyzed. Rough set theory is used to reduce the influencing factors of building energy consumption. Deep learning is used to extract features of building energy consumption data. An accuracy comparison of several prediction models based on rough sets and different neural networks is made.
Abstract The efficient and accurate prediction of building energy consumption can improve the management of power systems. In this paper, the rough set theory was used to reduce the redundant influencing factors of building energy consumption and find the critical factors of building energy consumption. These key factors were then used as the input of a deep neural network with a “deep” architecture and powerful capabilities in extracting features. Building energy consumption is output of the deep neural network. This study collected data from 100 civil public buildings for rough set reduction, and then collected data from a laboratory building of a university in Dalian for nearly a year to train and test deep neural networks. The test included both the short-term and medium-term predictions of building energy consumption. The prediction results of the deep neural network were compared with that of the back propagation neural network, Elman neural network and fuzzy neural network. The results show that the integrated rough set and deep neural network was the most accurate. The method proposed in this study could provide a practical and accurate solution for building energy consumption prediction.
A building energy consumption prediction model based on rough set theory and deep learning algorithms
Highlights The combination of rough set theory and deep learning algorithms is analyzed. Rough set theory is used to reduce the influencing factors of building energy consumption. Deep learning is used to extract features of building energy consumption data. An accuracy comparison of several prediction models based on rough sets and different neural networks is made.
Abstract The efficient and accurate prediction of building energy consumption can improve the management of power systems. In this paper, the rough set theory was used to reduce the redundant influencing factors of building energy consumption and find the critical factors of building energy consumption. These key factors were then used as the input of a deep neural network with a “deep” architecture and powerful capabilities in extracting features. Building energy consumption is output of the deep neural network. This study collected data from 100 civil public buildings for rough set reduction, and then collected data from a laboratory building of a university in Dalian for nearly a year to train and test deep neural networks. The test included both the short-term and medium-term predictions of building energy consumption. The prediction results of the deep neural network were compared with that of the back propagation neural network, Elman neural network and fuzzy neural network. The results show that the integrated rough set and deep neural network was the most accurate. The method proposed in this study could provide a practical and accurate solution for building energy consumption prediction.
A building energy consumption prediction model based on rough set theory and deep learning algorithms
Lei, Lei (author) / Chen, Wei (author) / Wu, Bing (author) / Chen, Chao (author) / Liu, Wei (author)
Energy and Buildings ; 240
2021-03-03
Article (Journal)
Electronic Resource
English
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