A Deep Learning Neural Network for the Residential Energy Consumption Prediction

2022 ◽  
Author(s):  
Jinhai Huang ◽  
Chengxin Pang ◽  
Weijun Yang ◽  
Xinhua Zeng ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Energy Consumption ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Energy Consumption Prediction ◽  
Deep Learning Neural Network ◽  
Consumption Prediction

scholarly journals Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

2019 ◽  
Vol 12 (1) ◽  
pp. 109 ◽  
Author(s):  
Mansu Kim ◽  
Sungwon Jung ◽  
Joo-won Kang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Energy Consumption ◽  
Prediction Models ◽  
Residential Buildings ◽  
Residential Energy ◽  
Artificial Neural ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.

scholarly journals Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

2020 ◽  
Vol 131 ◽  
pp. 109980 ◽  
Author(s):  
X.J. Luo ◽  
Lukumon O. Oyedele ◽  
Anuoluwapo O. Ajayi ◽  
Olugbenga O. Akinade ◽  
Hakeem A. Owolabi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Swarm Optimization ◽  
Al Composite ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

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