Electricity load profiles in Europe: The importance of household segmentation

Hayn, Marian; Bertsch, Valentin; Fïchtner, Wolf

doi:10.1016/j.erss.2014.07.002

Cited by 121 publications

(77 citation statements)

References 23 publications

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“…In general, an increase in household size often correlates with a higher total and lower per-capita energy consumption [11,33,38,39]. However, the effect of household size varies by household composition.…”

Section: Household Size and Compositionmentioning

confidence: 96%

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A structural equation model of energy consumption in the United States: Untangling the complexity of per-capita residential energy use

Estiri

2015

Energy Research & Social Science

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Section: Household Size and Compositionmentioning

confidence: 96%

“…Even with similar household characteristics, income positively correlates with energy consumption [4,8,9,20,33,[38][39][40][41][42]. However, prior research provides additional evidence that suggests the effect of income on energy consumption is more complex than a simple correlation, due to its effect on housing characteristics [11,36,43,44].…”

Section: Incomementioning

confidence: 98%

A structural equation model of energy consumption in the United States: Untangling the complexity of per-capita residential energy use

Estiri

2015

Energy Research & Social Science

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“…Other work has focused on typifying household load profiles into several categories [6], [7]. In a decentralized approach, different consumption profile types are important in regards to the possibility of forming groups of complementary types.…”

Section: Background and Related Workmentioning

confidence: 99%

A multi-scale energy demand model suggests sharing market risks with intelligent energy cooperatives

Methenitis

Kaisers

Poutré

2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Abstract-In this paper, we propose a multi-scale model of energy demand that is consistent with observations at a macro scale, in our use-case standard load profiles for (residential) electric loads. We employ the model to study incentives to assume the risk of volatile market prices for intelligent energy cooperatives at different aggregation scales of energy consumption. Next to scale, we investigate the benefits of demand response, more precisely intelligent scheduling of time-shiftable electric processes, and virtual storage intraday and between days. Results show that the increasing electrification and introduction of flexibilities (electric vehicles, thermal applications, storage, etc.) is going to make market participation viable for smaller groups of consumers. Retailers may thus introduce innovative tariffs for intelligent energy cooperatives to share the risk of volatility in wholesale markets for electricity.

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“…A detailed analyses for market segmentation is presented in Ref. [6] based on effect of lifestyles, socio-demographic factors, smart appliances, electricity and heat supply. Authors also identified the most influenced factors that determined the segmentation: socio-demographic factors (household size, net income and employment status), types of electric appliances and the use of new (smart) technologies.…”

Section: Determine Consumers' Profilesmentioning

confidence: 99%

Electricity Consumption and Generation Forecasting with Artificial Neural Networks

Bârã¹,

Oprea²

2018

Advanced Applications for Artificial Neural Networks

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Nowadays, smart meters, sensors and advanced electricity tariff mechanisms such as timeof-use tariff (ToUT), critical peak pricing tariff and real time tariff enable the electricity consumption optimization for residential consumers. Therefore, consumers will play an active role by shifting their peak consumption and change dynamically their behavior by scheduling home appliances, invest in small generation or storage devices (such as small wind turbines, photovoltaic (PV) panels and electrical vehicles). Thus, the current load profile curves for household consumers will become obsolete and electricity suppliers will require dynamical load profiles calculation and new advanced methods for consumption forecast. In this chapter, we aim to present some developments of artificial neural networks for energy demand side management system that determines consumers' profiles and patterns, consumption forecasting and also small generation estimations.

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Electricity load profiles in Europe: The importance of household segmentation

Cited by 121 publications

References 23 publications

A structural equation model of energy consumption in the United States: Untangling the complexity of per-capita residential energy use

A structural equation model of energy consumption in the United States: Untangling the complexity of per-capita residential energy use

A multi-scale energy demand model suggests sharing market risks with intelligent energy cooperatives

Electricity Consumption and Generation Forecasting with Artificial Neural Networks

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