The impacts of climatic and economic factors on residential electricity consumption of Bangkok Metropolis

Wangpattarapong, Kiattiporn; Maneewan, Somchai; Ketjoy, Nipon; Rakwichian, Wattanapong

doi:10.1016/j.enbuild.2008.01.006

Cited by 66 publications

(22 citation statements)

References 8 publications

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“…This condition suggests the use of heating degree days (HDD) and cooling degree days (CDD) [1], [4]. (1) (2) In this expression, Tre[1 and Trej2 are reference temperature for HDD and CDD respectively, and T is temperature. Figure 1 indicate to select Tre[1 = Trej2 = 18°C to calculate HDD andCDD.…”

Section: Introductionmentioning

confidence: 99%

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Elasticity coefficient of climatic conditions for electricity consumption analysis

Akil

Miyauchi

2010

2010 International Conference on Power System Technology

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The aim of this work is to analyze factors influencing electricity consumption in Japan using regression analysis. Every season regression models are developed for forecasting and determining elasticity coefficient associated with climatic conditions. As explanation variables, we use temperature, relative humidity and other factors such us holidays. Then, several statistical tests, for instance, t-test, adjusted coefficient R2, and Durbin-Watson statistic test, are used to validate the models. As an error analysis, MAPE is used to evaluate the forecasting performance. The results indicate that almost all explanation variables of the 4 seasonal models with different time period have significant influence. The models with autoregressive concerning the error term give better forecasting than without autoregressive term. Moreover, 2 scenarios are tested for each season to observe further the effect of changing climatic conditions to the electricity demand.

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Section: Introductionmentioning

confidence: 99%

“…Several researches have been done to investigate the relationship between the variable to the electricity consumption [1]- [4]. With regard to the effect on climatic conditions, elasticity coefficient [2] of the variable is important to observe how the variable influences electricity demand.…”

Section: Introductionmentioning

confidence: 99%

Elasticity coefficient of climatic conditions for electricity consumption analysis

Akil

Miyauchi

2010

2010 International Conference on Power System Technology

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“…As an explanation variable, meteorological parameters such as temperature, humidity, wind speed, and so on, are commonly used and confirmed electricity demand effectively. Prior studies which employed meteorological parameters and regression models for electricity demand can be found in references such as [1][2][3][4]. Reference [1] develops a demand model using a stepwise procedure to forecast Spanish daily electricity demand.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [2] develops regression equations to analyze electricity consumption for residential area in Hong Kong by using climatic and economic variables. Reference [3] develops the model of electricity consumption for residential area in Bangkok Metropolis and analyzes effect of climatic and economic factors for demand. Meanwhile, in [4], authors have developed two statistical models for demand in Greece, namely daily and monthly models to forecast demand up to 12 months ahead (mid-term demand).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Regression Models for Electricity Consumption Characteristics Analysis

Akil¹

2013

ENG

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This paper presents seasonal regression models of demand to investigate electricity consumption characteristics. Electricity consumption in commercial areas in Japan is analyzed by using meteorological variables, namely temperature and relative humidity. A dummy variable for holidays is also considered. We have developed models for two levels of period to analyze demand characteristics, that is, half year models and seasonal models. Some options for each model are calculated and validated by statistical tests to obtain better models. As results, half year and seasonal models present explicit information about how the variables affect the demand differently for each period. These specific information help in analyzing characteristics of studied commercial demand.

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Introduction: High-Rise Urban Form and Microclimate

Yang

Chen

2019

The Urban Book Series

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The impacts of climatic and economic factors on residential electricity consumption of Bangkok Metropolis

Cited by 66 publications

References 8 publications

Elasticity coefficient of climatic conditions for electricity consumption analysis

Elasticity coefficient of climatic conditions for electricity consumption analysis

Seasonal Regression Models for Electricity Consumption Characteristics Analysis

Introduction: High-Rise Urban Form and Microclimate

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