Seasonal Peak Electricity Demand Characteristics: Japan Case Study

Akil, Yusri Syam

doi:10.11648/j.ijepe.20130203.18

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…Beside demand models are not difficult to compose, obtained regression results provide specific information about direct relationship between observed electricity demand and considered variables which is relatively easy to explain. The same (regression) approach and procedure has used effectively by one author in previous works for Japan context as found in [5,6]. Beside contribute to understand more load behavior, presented information can useful to manage electric power systems under tropical condition effectively and may help policy makers to design suitable regulation for system operation.…”

Section: Department Of Electrical Engineeringmentioning

confidence: 99%

The influence of meteorological parameters under tropical condition on electricity demand characteristic: Indonesia case study

Akil

Syafaruddin

Waris

et al. 2014

2014 the 1st International Conference on Information Technology, Computer, and Electrical Engineering

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As meteorological conditions can be unique in different countries and may have influence on electricity demand, providing demand model to analyze characteristic of demand is useful as obtained information can be used to manage related power systems better. This paper proposes regression based demand model to identify typical characteristic of demand in Indonesia more detail. Three different demand areas (Racing Area, Poltek-Area, and Paropo Area) in Makassar, Indonesia including their total demand (Total-Area) are analyzed by creating demand model. The demands are correlated with meteorological parameters (temperature functions, relative humidity, and wind speed) and holidays. Individual characteristics are firstly observed to obtain main drivers and their typical effect on each demand area. Furthermore, general characteristics are analyzed to find common characteristic of demand such as what variables influence electricity demand generally. Several options for model are calculated and assessed by statistical tests to get best model. Results indicate more information concerning characteristic of demands can be revealed by models which are well validated. Each demand area has individual characteristic as demand drivers and their effect are relatively different between areas. Other results concerning general characteristic confirm temperature functions, relative humidiy, and holidays are important driver for demand. The variables are quite good to explain electricity demand generally as adjusted coefficient of determination of model (R 2 , ) is 76.42%.An electric power system is expected can effectively service load demand in all time during its operation. To achieve such expected condition, knowledge about characteristics of connected demand in the system is an important thing, as it can be used by power utilities to manage their power systems better. By performing electricity demand analysis particularly characteristic analysis, typical information such as key drivers for demand and how far their effect under a certain condition are possibly known. One of the method that can be used for the task above is regression approach. However, providing a good demand model as analysis tool is not an easy task as load 978·1·4799-6432-1114/$31.00 mOl41EEE 383

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Section: Department Of Electrical Engineeringmentioning

confidence: 99%

The influence of meteorological parameters under tropical condition on electricity demand characteristic: Indonesia case study

Akil

Syafaruddin

Waris

et al. 2014

2014 the 1st International Conference on Information Technology, Computer, and Electrical Engineering

Self Cite

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“…From Table 1, hourly peak and off-peak demand models for residential and commercial areas are composed for comparison analysis. Applied variables in the hourly models especially for temperature variables (CDD and/or HDD) are based on the temperature condition in each season [8]. As extended from paper [8], some new analyses are presented in this study including introducing a new variable (wind speed) in the developed models with focus on the seasonal peak and off-peak characteristics comparison between observed electricity demand areas.…”

Section: Peak and Off-peak Models For Residential And Commercial Areamentioning

confidence: 99%

“…Besides, a representative hour for off-peak demand in both areas is also analyzed. Regression based hourly demand models [8] are used in this characteristics comparison study. Presented results may useful to manage power systems more effective especially to meet seasonal peaks (and off-peaks) from different areas in the same place.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Peak Characteristic Comparison Analysis by Hourly Electricity Demand Model

Akil¹,

Miyauchi²

2014

IJEPE

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As driver variables may influence more than one area of electricity demand, knowing typical effect of the variables on certain demand areas is important. Utilities can use the information of managing power systems to meet electricity demand for different areas more effective. Based on the regression analysis approach, this study presents a peak demand characteristics comparison between Japanese residential and commercial areas in seasonal level by composing hourly electricity demand model for each area. Besides, a representative hour for off-peak demands is also analyzed. Similar variables (temperature, humidity, wind speed, and holidays) are applied to explain peak and off-peak of summer, autumn, winter, and spring in both areas. Results indicate key drivers for peak and off-peak demands are not same in the certain seasons for both areas. Obtained key variables tend to affect stronger peaks and off-peaks for residential than for commercial area in four observed seasons.

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“…Next, investigation of typical load characteristics particularly during daytime maximum demand period is done. From the previous work, one author has analyzed the peak electricity demand in Japan [7], [8]. It is hoped, this current research can contribute in managing power system under tropical condition mainly during maximum demand periods more effective, and further may help in determining proper needed actions to keep service to electric consumers.…”

Section: Introductionmentioning

confidence: 99%

Meteorological Parameters and Daytime Maximum Electricity Demand in Makassar, Indonesia

Akil¹,

Syafaruddin²,

Waris³

et al. 2015

IJMMM

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Abstract-As in the peak period electricity demand is high, providing a demand model to analyze load condition particularly during maximum demand period is important in relation to keep continuity service to connected consumers. In this research, effect of meteorological variables and holidays particularly on daytime maximum demand period for a total load for five different demand areas in Makassar -Indonesia are observed by developed demand models based on regression method using hourly data. We firstly observe characteristics of demand for whole period through WPED m model as a basis analysis. Next, we explore typical characteristic when daytime maximum demand period by creating DMD m model. In order to find better model, some options for each model are composed and assessed by standard statistical tests. From results, it is confirmed the composed models are well validated. Load drivers and their effect on the observed demand periods are found unique.Index Terms-Demand characteristic, daytime maximum demand, meteorological variables, electricity demand model, Makassar.

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Seasonal Peak Electricity Demand Characteristics: Japan Case Study

Cited by 7 publications

References 11 publications

The influence of meteorological parameters under tropical condition on electricity demand characteristic: Indonesia case study

The influence of meteorological parameters under tropical condition on electricity demand characteristic: Indonesia case study

Seasonal Peak Characteristic Comparison Analysis by Hourly Electricity Demand Model

Meteorological Parameters and Daytime Maximum Electricity Demand in Makassar, Indonesia

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