Occupancy analysis in commercial building cooling energy modelling with domestic water and electricity consumption

2021 ◽  
Vol 253 ◽  
pp. 111534
Author(s):  
Hongxiang Fu ◽  
Shinwoo Lee ◽  
Juan-Carlos Baltazar ◽  
David E. Claridge
Keyword(s):  
Electricity Consumption ◽  
Commercial Building ◽  
Domestic Water ◽  
Energy Modelling ◽  
Building Cooling

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.

Energy demand projections and relevance of income dynamics in Gauteng’s residential sector

2011 ◽  
Vol 22 (4) ◽  
pp. 31-47 ◽  
Author(s):  
Mamahloko Senatla
Keyword(s):  
Energy Consumption ◽  
Low Income ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Planning System ◽  
Strategy Development ◽  
Computing Power ◽  
Residential Sector ◽  
Energy Modelling ◽  
Main Driver

Energy modelling serves as a crucial tool for informing both energy policy and strategy development. But the modelling process is faced with both sectoral energy data and structural challenges. Among all the sectors, the residential sector usually presents a huge challenge to the modelling profession due to the dynamic nature of the sector. The challenge is brought by the fact that each an every household in a region may have different energy consumption characteristics and the computing power of the available models cannot incorporate all the details of individual household characteristics. Even if there was enough computing power within the models, energy consumption is collected through surveys and as a result only a sample of a region is captured. These challenges have forced energy modellers to categorise households that have similar characteristics. Different researchers choose different methods for categorising the households. Some researchers choose to categorise households by location and climate, others choose housing types while others choose quintiles. Currently, there is no consensus on which categorisation method takes precedence over others. In these myriad ways of categorising households, the determining factor employed in each method is what is assumed to be the driver of energy demand in that particular area of study. Many researchers acknowledge that households’ income, preferences and access to certain fuels determine how households use energy. Although many researchers recognise that income is the main driver of energy demand in the residential sector, there has been no energy modelling study that has tried to categorise households by income in South Africa. This paper chose to categorise households by income because income is taken to be the main driver of energy demand in the urban residential sector. Gauteng province was chosen as a case study area for this paper. The Long-range Energy Alternatives Planning System (LEAP) is used as a tool for such analysis. This paper will further reveal how the dynamics of differing income across the residential sector affects total energy demand in the long run. The households in Gauteng are classified into three income categories – high, middle and low income households. In addition to different income categories, the paper further investigates the energy demand of Gauteng’s residential sector under three economic scenarios with five energy demand scenarios. The three economic scenarios are first economic scenario (ECO1), second economic scenario (ECO2) and third economic scenario (ECO3). The most distinguishing factor between these economic scenarios is the mobility of households from one income band to the next.The model results show that electricity demand will be high in all the three economic scenarios. The reason for such high electrical energy demand in all the economic scenarios compared to other fuels is due to the fact that among all the provinces, Gauteng households have one of the highest electricity consumption profiles. ECO2 showed the highest energy demand in all the five energy demand scenarios. This is due to the fact that the share of high income households in ECO2 was very high, compared to the other two economic scenarios. The favourable energy demand scenarios will be the Energy Efficiency and MEPS scenarios due to their ability to reduce more energy demand than other scenarios in all the three economic scenarios.

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 Evaluation of the Existing Solar Energy and Rainwater Potential in the Total Roof Area of Buildings: Izmit District Example

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Mehmet Kaya
Keyword(s):  
Solar Energy ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Existing Buildings ◽  
Domestic Water ◽  
Solar Energy Utilization ◽  
Central District ◽  
Roof Area

In this research, an exemplary study has been conducted in order to draw attention to the importance of evaluating the existing potential on the roofs of buildings. This research offers an evaluation of the existing solar energy and rainwater potential on the total roof area of the buildings in the Izmit district, which is a central district of Kocaeli province, one of the busiest centers of industry in Turkey. The calculations in this study were carried out by using the data obtained from various institutions. As a result of these calculations, the ratio of electrical energy that can be provided with photovoltaic systems on roofs to meet the annual electricity consumption of the district was found to be 203.581%, and the annual solar energy utilization rate for a family of 4 to bring 240 liters of daily use water temperature to 60°C with an 8 m2 collector area was calculated as 66%. In addition, the ratio of rainwater that can be collected from the total roof area of the existing buildings in the district to meet the domestic water consumed by the district was found to be 33.27%.

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.

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