scholarly journals Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xuenan Zhang ◽  
Jinxin Zhang ◽  
Jinhua Zhang ◽  
YuChuan Zhang
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Random Forest ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Data Set

As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability.

scholarly journals Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Buildings ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 204 ◽  
Author(s):  
Yang ◽  
Tan ◽  
Santamouris ◽  
Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Missing Data ◽  
Recurrent Neural Networks ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Data Set ◽  
Raw Data ◽  
Deep Recurrent Neural Network

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

scholarly journals Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 731 ◽  
Author(s):  
Sanghyuk Lee ◽  
Jaehoon Cha ◽  
Moon Keun Kim ◽  
Kyeong Soo Kim ◽  
Van Huy Pham ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
False Negative ◽  
Building Energy ◽  
Training Data ◽  
Superior Performance ◽  
Data Generation ◽  
Building Energy Consumption ◽  
Data Set ◽  
Input Variables

The importance of neural network (NN) modelling is evident from its performance benefits in a myriad of applications, where, unlike conventional techniques, NN modeling provides superior performance without relying on complex filtering and/or time-consuming parameter tuning specific to applications and their wider ranges of conditions. In this paper, we employ NN modelling with training data generation based on sensitivity analysis for the prediction of building energy consumption to improve performance and reliability. Unlike our previous work, where insignificant input variables are successively screened out based on their mean impact values (MIVs) during the training process, we use the receiver operating characteristic (ROC) plot to generate reliable data with a conservative or progressive point of view, which overcomes the issue of data insufficiency of the MIV method: By properly setting boundaries for input variables based on the ROC plot and their statistics, instead of completely screening them out as in the MIV-based method, we can generate new training data that maximize true positive and false negative numbers from the partial data set. Then a NN model is constructed and trained with the generated training data using Levenberg–Marquardt back propagation (LM-BP) to perform electricity prediction for commercial buildings. The performance of the proposed data generation methods is compared with that of the MIV method through experiments, whose results show that data generation using successive and cross pattern provides satisfactory performance, following energy consumption trends with good phase. Among the two options in data generation, i.e., successive and two data combination, the successive option shows lower root mean square error (RMSE) than the combination one by around 400~900 kWh (i.e., 30%~75%).

scholarly journals Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiancheng Liu ◽  
Congxiang Tian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Fast Training ◽  
Simulation Calculation ◽  
Monitoring Platform

With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.

Research on Energy Consumption Tread of Residential Building in Beijing Based on Energy Audit

2012 ◽  
Vol 174-177 ◽  
pp. 2057-2060 ◽  
Author(s):  
Qing Feng Wang ◽  
Yan Li Wang ◽  
De Ying Li ◽  
Wei Na
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Building Energy Consumption ◽  
Energy Audit ◽  
Building Energy Saving ◽  
Site Test ◽  
Energy Efficiency Standards

Beijing building energy efficiency work has been carried out for 20 years, which played a significant role in building energy saving. Based on the energy audit which is site test and statistics of building energy consumption analyses the energy consumption of residential building in Beijing. Discusses the residential building energy consumption characteristics and gives the energy consumption tread of residential building in Beijing. The results show that Beijing's residential building energy consumption per unit area is reduced year by year, which is mainly related with the implementation of the Beijing building energy efficiency standards.

Energy Efficiency Assessment Oriented Building Energy Consumption System Model

2013 ◽  
Vol 415 ◽  
pp. 734-740
Author(s):  
Yun Long Ma ◽  
Xiao Hua Chen ◽  
Bo Liu ◽  
Guo Feng Zhang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Conservation ◽  
Emission Reduction ◽  
Building Energy ◽  
Efficiency Index ◽  
Assessment System ◽  
Building Energy Consumption ◽  
Building Energy Conservation ◽  
Consumption System

This paper analyzes the characteristics and composition of the energy consumption system of the building from the perspective of systematic energy conservation and presents the systematic framework of the consumption model. Based on the framework, the paper focuses on how to establish a building energy consumption assessment system, find the energy efficiency index system and assessment approaches, and apply the results directly into building energy conservation and emission reduction. It not only facilitates greatly the overall and efficient management of the energy consumption system of the building, but also serves as another new approach to achieve energy conservation and emission reduction.

scholarly journals Long-Term Prediction of Weather for Analysis of Residential Building Energy Consumption in Australia

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4805
Author(s):  
Shu Chen ◽  
Zhengen Ren ◽  
Zhi Tang ◽  
Xianrong Zhuo
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Residential Building ◽  
Building Energy ◽  
Space Heating ◽  
Building Energy Consumption ◽  
Climate Zones ◽  
Impact Of Climate Change ◽  
Heating And Cooling ◽  
The Impact

Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses.

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