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Method for development and segmentation of load profiles for different final customers and appliances

Panel: 7. Monitoring and evaluation

This is a peer-reviewed paper.

Authors:
Andre Z. Morch, SINTEF Energy Research
, Norway
Hanne Sæle, SINTEF Energy Research, Norway
Nicolai Feilberg, SINTEF Energy Research, Norway
Karen Byskov Lindberg, The Norwegian Water Resources and Energy Directorate (NVE), Norway

Abstract

Implementation of smart metering in Europe creates new opportunities for studying electricity consumption patterns. Measurement of end-uses on appliance level remains however to be very time consuming and expensive, so the possibility to model segmented end-use demand based on metering of total electricity consumption is essential.

The paper presents results from a Norwegian Research project "Electricity Demand Knowledge - ElDeK" (2009-2012). A total of 75 Norwegian households from four electricity Distribution System Operators (DSOs) participated in the study. The project collected hourly time series of total electricity consumption from the households, and additional high-resolution (one minute) metered data of more than 500 different appliance-specific loads as water heaters, washing machines, television sets etc.

The collected data were validated and analysed by use of a software tool called Useload. Based on metered data of the total electricity consumption for the household, the project has developed a statistical method for segmenting the hourly metered consumption data into weather dependent (for example space heating) and weather independent loads. Additionally the weather-independent load has been further segmented into demands from appliances as lighting, refrigeration, water heating etc. Demand patterns of several households have been analysed, resulting in typical group- and household-specific demand profiles.

The new approach provides cost efficient and rapid statistical methods for development of detailed load profiles based essentially on metered data with resolution one hour or higher, collected by smart meters. It allows identifying a potential for goal-oriented energy efficiency actions and later verifying impacts of these. Division of the load between weather-dependent and independent segments identifies potential flexibility in consumption (Demand Response) and creates basis for load forecasts.

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