Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation

Widén, Joakim; Lundh, Magdalena; Vassileva, Iana; Dahlquist, Erik; Ellegård, Kajsa; Wäckelgård, Ewa

doi:10.1016/j.enbuild.2009.02.013

Cited by 334 publications

(198 citation statements)

References 10 publications

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“…These data have been analysed previously in a behavioural study connected to the measurement survey [18] and for validation of the load models in refs. [19,20,21]. They have therefore been carefully examined for consistency and quality.…”

Section: Electricity Demand Datamentioning

confidence: 99%

Impacts of different data averaging times on statistical analysis of distributed domestic photovoltaic systems

Widén

Wäckelgård

Paatero³

et al. 2010

Solar Energy

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The trend of increasing application of distributed generation with solar photovoltaics (PV-DG) suggests that a widespread integration in existing low-voltage (LV) grids is possible in the future. With massive integration in LV grids, a major concern is the possible negative impacts of excess power injection from on-site generation. For power-ow simulations of such grid impacts, an important consideration is the time resolution of demand and generation data. This paper investigates the impact of time averaging on high-resolution data series of domestic electricity demand and PV-DG output and on voltages in a simulated LV grid. E ects of 10-minutely and hourly averaging on descriptive statistics and duration curves were determined. Although time averaging has a considerable impact on statistical properties of the demand in individual households, the impact is smaller on aggregate demand, already smoothed from random coincidence, and on PV-DG output. Consequently, the statistical distribution of simulated grid voltages was also robust against time averaging. The overall judgement is that statistical investigation of voltage variations in the presence of PV-DG does not require higher resolution than hourly.1

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Section: Electricity Demand Datamentioning

confidence: 99%

Impacts of different data averaging times on statistical analysis of distributed domestic photovoltaic systems

Widén

Wäckelgård

Paatero³

et al. 2010

Solar Energy

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“…Information is collected regarding who is involved in specific energy-use-related activities and routines that involve various constellations of household members [16][17].…”

Section: A Bottom-up Approach To Energy Use and Everyday Lifementioning

confidence: 99%

Visualizing energy consumption activities as a tool for making everyday life more sustainable

2011

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social context) they are performed. We discuss the importance of relating information and feedback to households' everyday activities, in order to make it relevant to households. Through our method we discover and visualize activity patterns in a household during a given period. The method is also useful to households as a reflective tool when discussing families' daily lives in relation to energy consumption. The method gives direct feedback to households and the information is relevant since it emanates from their own reported activities.

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“…The novelty of this study is the combined use of a detailed and validated house model, measured high-resolution weather data and detailed load profiles for both DHW and electric load. The stochastical load patterns are based on monitoring data for single family houses (Widén et al, 2012(Widén et al, , 2009. All these are implemented in the simulation software TRNSYS where the components are designed to meet the specifications of existing products of a house, heat pump, hot water tank and battery storage available on the market today.…”

Section: Aim and Noveltymentioning

confidence: 99%

“…Two adults and two children are living in the house, and the internal heat energy gain, assumed to be the same as the electricity consumption for appliances, are calculated using a Markov-chain model for occupancy and energy use described in Widén et al (2009). The DHW demand is derived from the MacSheep project and adjusted for this study, see Table 2 ( Bales et al, 2015).…”

Section: House Model and Boundary Conditionsmentioning

confidence: 99%

Control Algorithms for PV and Heat Pump System Utilizing Thermal and Electrical Storage

Psimopoulos¹,

Leppin²,

Luthander³

et al. 2016

Proceedings of EuroSun2016

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In this study a detailed model of a single-family house with an exhaust air heat pump and photovoltaic system is developed in the simulation software TRNSYS. The model is used to evaluate three control algorithms using thermal and electrical storage in terms of final energy, solar fraction, self-consumption and seasonal performance factor. The algorithms are tested and compared with respect to energetic improvement for 1) use of the heat pump plus storage tank for domestic hot water and space heating, 2) use of the electrical storage in batteries and (3) use of both electrical and thermal storage. Results show the highest increase of self-consumption to 50.5%, solar fraction to 40.6% and final energy decrease to 6923 kWh by implementing the third algorithm in a system with 9.36 kW PV capacity and battery storage of 10.8 kWh. The use of electrical energy storage has higher positive impact compared to the thermal storage with the settings and component sizes used. The combined use of thermal storage and batteries leads to final energy savings that are nearly the same as the combined savings of thermal storage and batteries separately, showing that they are mostly independent of one another for the settings of this study.

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Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation

Cited by 334 publications

References 10 publications

Impacts of different data averaging times on statistical analysis of distributed domestic photovoltaic systems

Impacts of different data averaging times on statistical analysis of distributed domestic photovoltaic systems

Visualizing energy consumption activities as a tool for making everyday life more sustainable

Control Algorithms for PV and Heat Pump System Utilizing Thermal and Electrical Storage

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