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More or Less about Data - Analyzing Load Demand in Residential Houses

Abaravicius, Juozas LU ; Sernhed, Kerstin LU and Pyrko, Jurek LU (2006) ACEEE Summerstudy 2006 In [Host publication title missing]
Abstract
Load demand in residential houses is a significant contributor to peak load problems experienced by utilities. The knowledge about demand variation in households is fairly limited as well as the use of various tools to analyze the demand. Many utilities have recently installed interval (hourly) metering at their residential customers. The availability of hourly data is a significant progress, however, the utilities use this data only to a limited extent, mostly for billing purposes only. This study aims to discuss the possibilities and the benefits of using this valuable data. There are several established load analysis tools, such as load curve, typical load curve, load duration curve, load factor, superposition factor, etc., which... (More)
Load demand in residential houses is a significant contributor to peak load problems experienced by utilities. The knowledge about demand variation in households is fairly limited as well as the use of various tools to analyze the demand. Many utilities have recently installed interval (hourly) metering at their residential customers. The availability of hourly data is a significant progress, however, the utilities use this data only to a limited extent, mostly for billing purposes only. This study aims to discuss the possibilities and the benefits of using this valuable data. There are several established load analysis tools, such as load curve, typical load curve, load duration curve, load factor, superposition factor, etc., which utilities could apply and develop to provide feedback to small electricity users. Among other benefits, the hourly load data analysis can provide the detailed characteristics of load demand, define the consumption patterns and can help to identify which households contribute most to the utility peaks. This information is essential when developing new energy services, appropriate pricing, load management strategies and demand response programs. Through the analysis of strengths and weaknesses of different load analysis tools, this paper defines the knowledge they could give, how applicable they are and what value they could have both for the utility and the residential customer. The study is exemplified with ten cases of households with electric space heating in Southern Sweden. (Less)
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publication status
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in
[Host publication title missing]
publisher
ACEEE
conference name
ACEEE Summerstudy 2006
language
English
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no
id
679c38da-bd11-4687-9be1-3b317129a10b (old id 576564)
date added to LUP
2008-02-06 17:58:39
date last changed
2016-06-29 09:15:00
@inproceedings{679c38da-bd11-4687-9be1-3b317129a10b,
  abstract     = {Load demand in residential houses is a significant contributor to peak load problems experienced by utilities. The knowledge about demand variation in households is fairly limited as well as the use of various tools to analyze the demand. Many utilities have recently installed interval (hourly) metering at their residential customers. The availability of hourly data is a significant progress, however, the utilities use this data only to a limited extent, mostly for billing purposes only. This study aims to discuss the possibilities and the benefits of using this valuable data. There are several established load analysis tools, such as load curve, typical load curve, load duration curve, load factor, superposition factor, etc., which utilities could apply and develop to provide feedback to small electricity users. Among other benefits, the hourly load data analysis can provide the detailed characteristics of load demand, define the consumption patterns and can help to identify which households contribute most to the utility peaks. This information is essential when developing new energy services, appropriate pricing, load management strategies and demand response programs. Through the analysis of strengths and weaknesses of different load analysis tools, this paper defines the knowledge they could give, how applicable they are and what value they could have both for the utility and the residential customer. The study is exemplified with ten cases of households with electric space heating in Southern Sweden.},
  author       = {Abaravicius, Juozas and Sernhed, Kerstin and Pyrko, Jurek},
  booktitle    = {[Host publication title missing]},
  language     = {eng},
  publisher    = {ACEEE},
  title        = {More or Less about Data - Analyzing Load Demand in Residential Houses},
  year         = {2006},
}