Performance analyses of statistical approaches for modeling electricity consumption of a commercial building in France

2019 ◽  
Vol 195 ◽  
pp. 82-92 ◽  
Author(s):  
Sevcan Demir Atalay ◽  
Gulben Calis ◽  
Gozde Kus ◽  
Merve Kuru
Keyword(s):  
Electricity Consumption ◽  
Commercial Building ◽  
Performance Analyses ◽  
Statistical Approaches

scholarly journals Experimental Analysis on Solid Desiccant Used in An Air Conditioning

2019 ◽  
Vol 130 ◽  
pp. 01007
Author(s):  
Ekadewi Anggraini Handoyo ◽  
Andriono Slamet ◽  
Muhammad Danang Birowosuto
Keyword(s):  
Silica Gel ◽  
Electricity Consumption ◽  
Air Cooling ◽  
Air Conditioner ◽  
Vapor Compression ◽  
Commercial Building ◽  
Vapor Compression Refrigeration Cycle ◽  
Vapor Compression Refrigeration ◽  
Closed Door ◽  
Consumption Saving

Garden by The Bay in Singapore is the world’s largest coolest conservatories. Although it is located in tropics and uses so many glasses, its electricity consumption is as much as a commercial building. The key to this low consumption is in air cooling technology. Air used for cooling the conservatories is dehumidified first using liquid desiccants before cooled. The same technology was implemented to a single-split air conditioner (AC) that works on a vapor-compression refrigeration cycle. The experiments were conducted in a room with opened and closed door. Instead of using a liquid desiccant, the experiment used a solid desiccant, i.e., silica gel which thickness was 6 mm and 8 mm with density equals to 1.27 gr cm–3. From the experiment, it is found that: (i) the thicker the silica gel, the higher outlet air temperature from silica gel, (ii) less condensate will be produced when the silica gel used is thicker, (iii) silica gel is suitable for reducing humidity of outdoor/fresh air, and (iv) the electricity consumption saving for inserting 8 mm silica gel is only 4 % when the door is closed and 31 % when the door is opened.

scholarly journals Lighting control network based on KNX protocol, for the reduction of energy consumption

2020 ◽  
Vol 19 (3) ◽  
pp. 1186
Author(s):  
Omar Chamorro Atalaya ◽  
Angel Quesquen-Porras ◽  
Dora Arce Santillan
Keyword(s):  
Energy Consumption ◽  
Network Architecture ◽  
Electricity Consumption ◽  
Environmental Benefits ◽  
Control Network ◽  
Commercial Building ◽  
Lighting Control ◽  
Reduction Of Energy Consumption ◽  
Carbon Dioxide Co2 ◽  
Air Conditioning Systems

<span>This article presents the development of a lighting control network to reduce the energy consumption of a commercial building, using the KNX protocol; because of the high rates of electricity consumption, the same that are reflected in the payment of the electricity supply. For this, the design of the network architecture is carried out, the tree type quality and it has KNX, DALI components and LED luminaires, which are interconnected by means of an Ethernet type BUS; The KNX protocol configuration is then performed using the ETS version 5 software; carries out the implementation of KNX technology, determines the reduction of energy consumption by 82.33%. Likewise, emissions of carbon dioxide (CO2), one of the main gases involved in climate change, were reduced by 85%. With these results we obtain economic and environmental benefits; Reason why it is proposed to perform the same procedure for the control of air conditioning systems, since their operation represents 32.8% of the energy consumption of an establishment.</span>

scholarly journals Performance simulation of semi-transparent photovoltaic glass on a skylight for commercial building

2021 ◽  
Vol 12 ◽  
pp. e021009
Author(s):  
Leno Pôrto Dutra ◽  
Isabel Tourinho Salamoni ◽  
Eduardo Grala da Cunha
Keyword(s):  
Computational Simulation ◽  
Variable Parameter ◽  
Electricity Consumption ◽  
Electric Load ◽  
Research Subject ◽  
Commercial Building ◽  
Performance Simulation ◽  
Transparent Glass ◽  
Photovoltaic Conversion ◽  
Photovoltaic Technology

The demand for energy in buildings is a worldwide research subject due to its importance in the global electric load share. Besides, photovoltaic conversion to generate electricity locally is one of the ways to meet that demand. This work aims to evaluate the application of semi-transparent photovoltaic glass on a skylight of a commercial building and estimate electricity consumption and production using computational simulation with EnergyPlus. The opening size was set as a variable parameter. Its performance was compared to an ordinary skylight and opaque modules under the same conditions for three different Brazilian bioclimatic zones. Results show that the area's change provided significant differences in generation and less important ones in consumption. Among the bioclimatic zones, the building presented the lowest consumption and the highest generation for all configurations in the coldest zone, making it the best region for net electricity, i.e., purchased from a utility. Comparing semi-transparent with opaque modules, the latter produced much more energy, but consumption was reduced by an average of 28% in favour of the photovoltaic glass. The main conclusion is that the use of photovoltaic technology in a semi-transparent glass is promising regarding the integration of generators to the building, but the efficiency rates need to increase to bring it closer to opaque modules in performance.

DATA SELECTION TEST METHOD FOR BETTER PREDICTION OF BUILDING ELECTRICITY CONSUMPTION

2016 ◽  
Vol 78 (5-7) ◽  
Author(s):  
Iqbal Faridian Syah ◽  
Md Pauzi Abdullah ◽  
Husna Syadli ◽  
Mohammad Yusri Hassan ◽  
Faridah Hussin
Keyword(s):  
Prediction Accuracy ◽  
Historical Data ◽  
Moving Average ◽  
Electricity Consumption ◽  
Test Method ◽  
Data Selection ◽  
Commercial Building ◽  
Data Set ◽  
Selection Test ◽  
Accuracy Of Prediction

The issue of obtaining an accurate prediction of electricity consumption has been widely discussed by many previous works. Various techniques have been used such as statistical method, time-series, heuristic methods and many more. Whatever the technique used, the accuracy of prediction depends on the availability of historical data as well as the proper selection of the data. Even the data is exhaustive; it must be selected so that the prediction accuracy can be improved. This paper presented a test method named Data Selection Test (DST) method that can be used to test the historical data to select the correct data set for prediction. The DST method is demonstrated and tested on practical electricity consumption data of a selected commercial building. Three different prediction methods are used (ie. Moving Average, MA, Exponential Smoothing, ES and Linear Regression, LR) to evaluate the prediction accuracy by using the data set recommended by the DST method.  

scholarly journals Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1201 ◽  
Author(s):  
Moon Kim ◽  
Jaehoon Cha ◽  
Eunmi Lee ◽  
Van Pham ◽  
Sanghyuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Electricity Consumption ◽  
Shopping Mall ◽  
Commercial Building ◽  
Rapid Urbanization ◽  
Data Set ◽  
Training Time ◽  
Building Model

With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.

A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model

1993 ◽  
Vol 115 (2) ◽  
pp. 77-84 ◽  
Author(s):  
D. Ruch ◽  
Lu Chen ◽  
J. S. Haberl ◽  
D. E. Claridge
Keyword(s):  
Principal Component Analysis ◽  
Change Point ◽  
Electricity Consumption ◽  
Principal Component ◽  
Component Analysis ◽  
Commercial Buildings ◽  
Grocery Store ◽  
Commercial Building ◽  
Point Model ◽  
Change Point Model

A new method for predicting daily whole-building electricity usage in a commercial building has been developed. This method utilizes a Principal Component Analysis (PCA) of intercorrelated influencing parameters (e.g., dry-bulb temperature, solar radiation and humidity) to predict electricity consumption in conjunction with a change-point model. This paper describes the PCA procedure and presents the results of its application in conjunction with a change-point regression, to predict whole-building electricity consumption for a commercial grocery store. Comparison of the results with a traditional Multiple Linear Regression (MLR) analysis indicates that a change-point, Principal Component Analysis (CP/PCA) appears to produce a more reliable and physically plausible model than an MLR analysis and offers more insight into the environmental and operational driving forces that influence energy consumption in a commercial building. It is thought that the method will be useful for determining conservation retrofit savings from pre-retrofit and post-retrofit consumption data for commercial buildings. A companion paper presents the development of the four-parameter change-point model and a comparison to the Princeton Scorekeeping Method (PRISM) (Ruch and Claridge, 1991).

scholarly journals DETERMINING BARRIERS IN SUSTAINABLE REFURBISHMENT PROJECTS: COMMERCIAL BUILDING OWNER PERSPECTIVES

2017 ◽  
Vol 15 (3) ◽  
Author(s):  
Md Asrul Nasid Masrom ◽  
Mohd Hilmi Izwan Abd Rahim ◽  
Siow Chan Ann ◽  
Sulzakimin Mohamed ◽  
Kai Chen Goh ◽  
...  
Keyword(s):  
Qualitative Data ◽  
Electricity Consumption ◽  
Green Building ◽  
Commercial Buildings ◽  
Environmental Benefits ◽  
Commercial Building ◽  
Great Opportunity ◽  
Semistructured Interviews ◽  
Reduce Carbon Dioxide ◽  
Building Owner

Existing commercial buildings in Malaysia account for one third of the total electricity consumption. They present a great opportunity for refurbishment to reduce carbon dioxide. However, sustainably refurbished commercial buildings that is Green Building Index (GBI) certified is still low. This study aims to investigate barriers that impede commercial building owners to implement sustainable refurbishment and explore ways to increase numbers of sustainable refurbishment implementation. Qualitative data were collected through semistructured interviews. The respondents were commercial building owners’ representative, who were mainly building managers. The findings indicated that higher cost of sustainable refurbishment than conventional method and lacking of sustainability awareness among Malaysian were the main barriers of sustainable refurbishment. This study will be useful in helping building owners and developers to enhance economic, social and environmental benefits of sustainable refurbishment.

Ray of Hope: Hopelessness Increases Electricity Consumption for Lighting

2014 ◽  
Author(s):  
Ping Dong ◽  
Xun (Irene) Huang ◽  
Chen-Bo Zhong
Keyword(s):  
Electricity Consumption
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