An Artificial Neural Network-Based Prediction of Government-Owned Building Energy Consumption with Design Variables

ICSDEC 2012 ◽  
2012 ◽  
Author(s):  
H. Son ◽  
S. Lee ◽  
C. Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Design Variables ◽  
Artificial Neural

Prediction of Building Energy Consumption at Early Design Stage Based on Artificial Neural Network

2010 ◽  
Vol 108-111 ◽  
pp. 580-585 ◽  
Author(s):  
Jian Yao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Building Energy ◽  
Building Envelope ◽  
Simulation Software ◽  
Design Stage ◽  
Early Design Stage ◽  
Heating And Cooling ◽  
Artificial Neural

In this study, the main objective is to predict buildings heating and cooling energy consumption benefitting from 18 building envelope performance parameters by using artificial neural network. A backpropagation neural network has been preferred and the data have been presented to network by being normalized. 7 Cases application study was carried out with conventional methods. The building energy simulation software DeST was used for the calculations of energy consumption and ANN toolbox of MATLAB was 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 successful prediction rate of over 97% and will be helpful for designers in designing period of buildings.

Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

2017 ◽  
Vol 11 (4) ◽  
pp. 522-540 ◽  
Author(s):  
Isham Alzoubi ◽  
Mahmoud Delavar ◽  
Farhad Mirzaei ◽  
Babak Nadjar Arrabi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Compressibility Factor ◽  
Imperialist Competitive Algorithm ◽  
Content Type ◽  
Competitive Algorithm ◽  
Land Leveling ◽  
Soil Compressibility ◽  
Artificial Neural

Purpose This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling. Design/methodology/approach Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated. Findings According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R2) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively. Originality/value A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
David Palchak ◽  
Siddharth Suryanarayanan ◽  
Daniel Zimmerle
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Time Of Day ◽  
University Campus ◽  
Electrical Load ◽  
Ann Model ◽  
Short Term ◽  
Management Options ◽  
Artificial Neural

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error; the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-h period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of noncritical loads, and availability of time of use (ToU) pricing, the possible demand-side management options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

scholarly journals A NILM method for cooling load disaggregation based on artificial neural network

2019 ◽  
Vol 111 ◽  
pp. 05020 ◽  
Author(s):  
Ziwei Xiao ◽  
Jiaqi Yuan ◽  
Wenjie Gang ◽  
Chong Zhang ◽  
Xinhua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Building Energy Management ◽  
Load Disaggregation ◽  
Artificial Neural ◽  
Cooling Loads

The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

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