Investigation on the Energy Consumption of Department Store in Thailand

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2018 5th International Conference on Power and Energy Systems Engineering, CPESE 2018, 2018 5th International Conference on Power 2018, and Energy Systems 19–21 September Nagoya, Japan Engineering, CPESE 2018, 19–21 September 2018, Nagoya, Japan

Investigation on the Energy Consumption of Department Store in Investigation on International the Energy Consumption Department The 15th Symposium on Districtof Heating and CoolingStore in Thailand Thailand Assessing the feasibility of Wongsapai using theb and heat demand-outdoor a, Det Damrongsaka,*, Wongkot Nattanee Thinateaa b Det Damrongsak Wongsapai and Nattanee Thinate forecast temperature function *, forWongkot a long-term district heat demand Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200 Thailand a

b

EnergyaDepartment Technology of forMechanical Environment Research Center, of Engineering, Mai University, Mai, Thailand 50200 Thailand Engineering, FacultyFaculty of Engineering, ChiangChiang Mai University, ChiangChiang Mai, 50200 a,b,c a a b c c b Energy Technology for Environment Research Center, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200 Thailand

I. Andrić

a

*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract This article investigated on the energy consumption of department stores in Thailand. Multiple linear regression has been developed find the relationship between electrical energy consumption various variables. was found that has electrical This articletoinvestigated on the energy consumption of department stores and in Thailand. Multiple Itlinear regression been energy consumption obtained from the forecast model were veryconsumption close to electrical energy variables. consumption fromfound the actual The developed to find the relationship between electrical energy and various It was that data. electrical Abstract difference of electrical energyfrom consumption from between the forecast model andenergy actualconsumption data is approximately 2%. data. Thus The the energy consumption obtained the forecast model were very close to electrical from the actual developed regression could be served from as a baseline used tomodel forecast energy in department difference of electricalmodel energy consumption betweensitting the forecast and actualconsumption data is approximately 2%.stores Thus and the District heating networks are commonly addressed in the literature one ofconsumption the mostconsumption effective solutions for decreasing the determine which variables have major impact on achanges insitting the electrical in department stores. developed regression model could be served as baseline used as toenergy forecast energy in department stores and greenhouse gas emissions frommajor the building sector. These require high consumption investments which are returned through the heat determine which variables have impact on changes in systems the electrical energy in department stores. to the changed climate conditions ©sales. 2018 Due The Authors. Published by Elsevier Ltd. and building renovation policies, heat demand in the future could decrease, © 2019 The Authors. Published by Elsevier Ltd. prolonging the investment return period. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier Ltd. This an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Theis scope of thisarticle paper is to assess feasibility of using(https://creativecommons.org/licenses/by-nc-nd/4.0/) the heat demand –Conference outdoor temperature for heatSystems demand Selection and peer-review under responsibility of thelicense 2018 5th International on Powerfunction and Energy This ismain an open access under the CCthe BY-NC-ND Selection and peer-review under responsibility of the 2018 5th International Conference on Power Energy Systems Engineering, forecast. and TheCPESE district2018, of Alvalade, located 2018, in Lisbon (Portugal), was used as aConference case study.and The district is Energy consisted of 665 Engineering, 19–21 September Nagoya, Japan. Selection peer-review under responsibility of the 2018 5th International on Power and Systems CPESE 2018, 19–21 2018, Nagoya, Japan. typology. Three weather scenarios (low, medium, high) and three district buildings that vary September in both19–21 construction period Engineering, CPESE 2018, September 2018,and Nagoya, Japan. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: Energy conservation; Energy; Building comparedEnergy with results from aEnergy; dynamic heat demand model, previously developed and validated by the authors. Keywords: conservation; Building The results showed that when only weather change is considered, the margin of error could be acceptable for some applications error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Thailand has been increasing the energy use every year especially electrical energy consumption. The decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Thailand sector has been the energy use every especially electrical energy consumption. The commercial was increasing accountable of intercept allyear electricity Thailand The final energy renovation scenarios considered). On the for otherabout hand, 34% function increaseduse for in 7.8-12.7% per [1]. decade (depending on the commercial sector was accountable for about 34% of all electricity use in Thailand [1]. The final energy coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

© 2017 The Authors. Published by Elsevier Ltd. * Corresponding author. Tel.: +66-5394-4146 ; fax: +66-5394-4145. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and address: [email protected] * E-mail Corresponding author. Tel.: +66-5394-4146 ; fax: +66-5394-4145. Cooling. E-mail address: [email protected] 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. Keywords: Heat demand; Forecast; Climate change This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1876-6102 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the 2018 5th International Conference on Power and Energy Systems Engineering, CPESE This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2018, 19–21 2018, Nagoya, Japan. of the 2018 5th International Conference on Power and Energy Systems Engineering, CPESE Selection andSeptember peer-review under responsibility 2018, 19–21 September 2018, Nagoya, Japan. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the 2018 5th International Conference on Power and Energy Systems Engineering, CPESE 2018, 19–21 September 2018, Nagoya, Japan. 10.1016/j.egypro.2018.11.131

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consumption in commercial sector increased by 2.9 percent in consistent with the growth of gross domestic product in the country by 3.9 percent in 2017 [2]. The improvement of energy consumption in this commercial sector was necessary and challenging for the investigation. The investigation focused on the energy baseline in department stores which were estimated 13% of the energy consumption and around 7% of GDP from the total commercial sector in Thailand. A mathematical model for forecasting energy consumption in the building based on measured data had been developed through the multiple regression analysis for many commercial office buildings in Hong Kong [3]. A benchmarking process for energy efficiency using multiple regression analysis of Energy Use Intensity (EUI) and its related factors had been obtained. The outcomes from this regression model and benchmarking system might be utilized in the policy analyses [4]. As department stores commonly used electrical equipment for many purposes such as air-conditioning system and lighting system, the study focused on electrical energy consumption and correlated variables in department stores in Thailand. Multiple linear regression was employed to investigate the electrical energy consumption and its related factors that might influence considerably on the energy consumption. The results from the finding provided the baseline for the efficient energy consumption in department stores in Thailand in the future. 2. Background Electrical energy is a primary source of energy used in the department stores in Thailand. Air conditioning and lighting systems generally take the major portion of the electricity use in department stores and most office buildings. The study focuses on three department stores in Thailand with the total electrical energy consumption of 789.72 TJ. Fig. 1 shows the portion of the energy consumption by engineering systems. Air-conditioning system, lighting system, and other systems are accountable for 68.90%, 11.90% and 19.20% of the total electrical energy consumption, respectively.

Fig. 1. Portion of the energy consumption by engineering system.

3. Methodology 3.1. Multiple regression analysis Mathematical regression analysis is normally used method for analysis of relationships between multiple variables. By identifying reasonable dependent relations between two or more variables, regression analysis helps in discovering the hidden rules from the data [5]. The multiple linear regression equation is as follows:

Y  b0  b1 ( X 1 )  b2 ( X 2 )  b3 ( X 3 )  ...  bn ( X n ) 

(1)



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Where Y is the predicted or expected value of the dependent variable, X1 through Xn are n distinct independent or predictor variables, b0 is the value of Y when all of the independent variables (X1 through Xn) are equal to zero, and b1 through bn are the estimated regression coefficients [6]. The residual  is used to test the overall significance (Ftest) of the equation. Another important indicator is the coefficient of determination, R2, which not only indicates the goodness of fit, but also can be interpreted as the amount of variations of the dependent variables explained by the regression equation [7]. 3.2. Energy baseline Baseline is a set of critical observations or data used as a basis for comparison or control [8]. It is applied for the selection of the most appropriate pathway. The selected pathway will be employed to create a baseline sitting which is suitable for building control. To make baseline sitting, it requires considering the difference of variations in energy consumption and factors affecting the energy use, capacity in monitoring and report of building control [9]. 4. Results and discussion The regression model was developed from the involved energy variables from three large department stores in Thailand. Thus this regression model might be served as a baseline sitting used to forecast the electrical energy consumption in department stores in the future. All variables used for constructing the regression model are illustrated in Table 1. Table 1. Dependent variable (Y) and independent variables (X). Variable

Description

Unit

Y X1 X2 X3 X4 X5 X6

Electrical energy consumption

kWh

Shopping area for rent

m2

Food court area

m2

Cinema area

m2

Center area

m2

Hall area

m2

Parking spaces in the buildings

m2

X7

Ambient temperature

o

X8

Relative humidity

%

C

All variables shown in Table 1 were collected from actual data. Shopping area for rent, food court area, cinema area, center area, hall area, parking spaces in the buildings were collected from surveyed data and from the department store annual energy management report submitted to Department of Alternative Energy Development and Efficiency, Ministry of Energy of Thailand every year. The ambient temperature and relative humidity were obtained directly from the Thai Meteorological Department. Multiple regression analysis was used to examine the relationship between electrical energy consumption and related independent variables. As a result, the coefficient values of each of the independent variables were obtained as illustrated in Table 2. Regression analysis showed that food court area (X2), cinema area (X3) and parking spaces in the buildings (X6) had insignificant impact on the electrical energy consumption (Y) as a result of a very small coefficient value. Thus food court area, cinema area and parking spaces in the buildings would be neglected. Other variables that had impact on electrical energy consumption were shopping area for rent, center area, hall area, ambient temperature and relative humidity. Ambient temperature (X7) did influence the electrical energy consumption from air-conditioning system in the department stores. Since the coefficient value of ambient temperature was high, it certainly affected the amount of electrical

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energy consumption of the department stores even with small changes in the ambient temperature. A well designed building and well energy management must be met in order to reduce the heat loads from outside getting into the building, and to succeed with the energy conservation. Table 2. Coefficient values of independent variables (X). Variable

Description

Coefficient value

b0

Constant

b1 b4 b5 b7 b8

Shopping area for rent

14.94

Center area

44.97

Hall area

175.51

-3,057,873.02

Ambient temperature

93,582.31

Relative humidity

8,724.47

According to the multiple linear regression of the electrical energy consumption and independent variables for department stores, it was found that the forecast model of the electrical energy consumption (Y) with respect to independent variables provided in Table 2 is given by Eq. (2).

Y  -3,057,873 .02  14.94 X 1   44.97 X 4   175.51 X 5   93,582.31 X 7   8,724.47 X 8 

(2)

Fig. 2 shows the electrical energy consumption obtained from the multiple regression model and the collected actual data in the department stores over 3-year period from 2015 to 2017. The electrical energy consumption derived from the forecast model follows closely to electrical energy consumption from the actual data. The electrical energy consumption from the forecast model is approximately 2% different from the energy consumption from the actual data. Therefore, this regression model can be used to forecast the electrical energy consumption in department stores. In addition, this model can be used as an alternative tool to analyze the electrical energy consumption with respect to related variables. It is essential to understand what variables play major contribution to changes in the electrical energy consumption in the department stores.

Fig. 2. Energy consumption from regression model and actual data.



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5. Conclusions An investigation on the electrical energy consumption of department stores in Thailand has been carried out. Multiple linear regression has been developed to find the electrical energy consumption with respect to several variables, i.e., shopping area for rent, center area, hall area, ambient temperature and relative humidity. The study shows the comparison of the actual electrical energy consumption and the forecasted electrical energy consumption from the regression model. The difference between the electrical energy consumption from the forecast model and the one from actual data is approximately 2%. The developed regression model can be served as a baseline sitting used to forecast the energy consumption in department stores. A good regression model may not be obtained for smaller department stores, where most data are not well organized and recorded due to a lack of personnel, equipment and financial resources. Therefore, the quality of collected data is the most dominant factor that determines how accurate the regression model is. Acknowledgements Technical data and assistances from Energy Technology for Environment Research Center, Faculty of Engineering, Chiang Mai University are gratefully acknowledged. References [1] Department of Alternative Energy Development and Efficiency, “Energy balance of Thailand 2016,” Ministry of Energy, Thailand (2016). [2] Department of Alternative Energy Development and Efficiency, “Annual energy management report,” Ministry of Energy, Thailand (2016). [3] Jing R., Wang M., Zhang R., Li N., Zhao Y., “A study on energy performance of 30 commercial office buildings in Hong Kong,” Energy and Buildings, 144, 117-128 (2017). [4] Chung W., Hui Y.V., Lam Y.M., “Benchmarking the energy efficiency of commercial buildings,” Applied Energy, 83, 1-14 (2006). [5] Tian Q., “Application of the method of mathematical regression to financial status analysis,” Chemical Engineering Transactions, 51, 703-708 (2016). [6] Yang N., Li Z., Feng Y., “Empirical study for influencing factors on environmental accounting information disclosure in chemical industry,” Chemical Engineering Transactions, 62, 1591-1596 (2017). [7] Montgomery D.C., Peck E.A., Vining G.G., “Introduction to Linear Regression Analysis,” John Wiley & Sons, Inc., Hoboken, New Jersey, USA (2006). [8] Moriarty J.P., “A theory of benchmarking,” Benchmarking: An International Journal, 18(4), 588-611 (2011). [9] Thinate N., Wongsapai W., Damrongsak D., “Energy performance study in Thailand hospital building,” Energy Procedia, 141, 255–259 (2017).