Building Energy Consumption Prediction Research Articles

Prediction of building energy consumption by using artificial neural networks

In this study, the main objective is to predict buildings energy needs benefitting from orientation, insulation thickness and transparency ratio by using artificial neural networks. A backpropagation neural network has been preferred and the data have been presented to network by being normalized. The numerical applications were carried out with finite difference approach for brick walls with and without insulation of transient state one-dimensional heat conduction. Three different building samples with different form factors (FF) were selected. For each building samples 0–2.5–5–10–15 cm insulations are assumed to be applied. Orientation angles of the samples varied from 0° to 80° and the transparency ratios were chosen as 15–20–25%. A computer program written in FORTRAN was used for the calculations of energy demand and ANN toolbox of MATLAB is used for predictions. As a conclusion; when the calculated values compared with the outputs of the network, it is proven that ANN gives satisfactory results with deviation of 3.43% and successful prediction rate of 94.8–98.5%.

Prediction of Residential Building Energy Consumption in Jilin Province, China

This research investigates and analyzes the current condition of urban and rural residential energy consumption of Jilin Province, China, by establishing a model of residential energy demand. The model can predict such energy demand based on the demand types and environmental load of Jilin Province until 2020. Compared with 2000, rural residential energy demand will reduce approximately 25% in 2020, because of the decrease of rural population per household, while the urban residential energy demand will grow about 2.5 times in 2020, because of the increased urban population and rising standard of living. At the same time, compared with 2000, CO2 emissions resulting from rural residential energy consumption will grow approximately 10% in 2020. The basic reason for this is the increase of electricity consumption by rural residents. CO2 emissions from urban residential energy consumption will grow about three times in 2020.

06/00462 Applying support vector machines to predict building energy consumption in tropical region: Dong, B. et al. Energy and Buildings, 2005, 37, (5), 545–553

06/00462 Applying support vector machines to predict building energy consumption in tropical region: Dong, B. et al. Energy and Buildings, 2005, 37, (5), 545–553

Applying support vector machines to predict building energy consumption in tropical region

The methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). This paper presents support vector machines (SVM), a new neural network algorithm, to forecast building energy consumption in the tropical region. The objective of this paper is to examine the feasibility and applicability of SVM in building load forecasting area. Four commercial buildings in Singapore are selected randomly as case studies. Weather data including monthly mean outdoor dry-bulb temperature ( T 0), relative humidity (RH) and global solar radiation (GSR) are taken as three input features. Mean monthly landlord utility bills are collected for developing and testing models. In addition, the performance of SVM with respect to two parameters, C and ɛ, was explored using stepwise searching method based on radial-basis function (RBF) kernel. Finally, all prediction results are found to have coefficients of variance (CV) less than 3% and percentage error (%error) within 4%.

BECON: A Program for Predicting Building Energy Consumption for Air-Conditioning Buildings

An air-conditioning system simulation program BECON has been developed for predicting energy consumption for airconditioning in buildings. BECON relies on a buiiding heat transfer simulation pragram HTB2 to provide the input of hourly space cooling loads of a building. The two programs are adapted as the computing tool in HK-BEAM, the Hong Kong Building Environmental Assessment Method, launched in December 1996. in the paper, the range of system and equipment models included in BECON, the methods it uses to model the performance of air-conditioning systems, and the functions of its front-end program are described.

Building Energy Use Prediction and System Identification Using Recurrent Neural Networks

Following several successful applications of feedforward neural networks (NNs) to the building energy prediction problem (Wang and Kreider, 1992; JCEM, 1992, 1993; Curtiss et al., 1993, 1994; Anstett and Kreider, 1993; Kreider and Haberl, 1994) a more difficult problem has been addressed recently: namely, the prediction of building energy consumption well into the future without knowledge of immediately past energy consumption. This paper will report results on a recent study of six months of hourly data recorded at the Zachry Engineering Center (ZEC) in College Station, TX. Also reported are results on finding the R and C values for buildings from networks trained on building data.