Advanced control strategy of a solar domestic hot water system with a segmented auxiliary heater

Prud’homme, T.; Gillet, Denis

doi:10.1016/s0378-7788(00)00107-9

Cited by 29 publications

(14 citation statements)

References 2 publications

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“…It is difficult to predict how a user will regulate the thermostat or even eventually change its own consumption pattern with such auxiliary controls [32]. More advanced controls such as predictive behaviour controls could be further investigated and tested in a real application [33].…”

Section: Discussionmentioning

confidence: 99%

Retrofitting Conventional Electric Domestic Hot Water Heaters to Solar Water Heating Systems in Single-Family Houses—Model Validation and Optimization

Bernardo

2013

Energies

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System cost reductions and development of standardised plug-and-function systems are some of the most important goals for solar heating technology development. Retrofitting hot water boilers in single-family houses when installing solar collectors has the potential to significantly reduce both material and installation costs. In this study, the TRNSYS simulation models of the retrofitting solar thermal system were validated against measurements. Results show that the validated models are in good agreement with measurements. On an annual basis a deviation of 2.5% out of 1099 kWh was obtained between the auxiliary energy from results and from the simulation model for a complete system. Using the validated model a system optimization was carried out with respect to control strategies for auxiliary heating, heat losses and volume of auxiliary storage. A sensitivity analysis was carried out regarding different volumes of retrofitted hot water boiler, DHW profiles and climates. It was estimated that, with adequate improvements, extended annual solar fractions of 60%, 78% and 81% can be achieved for Lund (Sweden), Lisbon (Portugal) and Lusaka (Zambia), respectively. The correspondent collector area was 6, 4 and 3 m 2 , respectively. The studied retrofitted system achieves a comparable performance with conventional solar thermal systems with the potential to reduce the investment cost.

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Section: Discussionmentioning

confidence: 99%

Retrofitting Conventional Electric Domestic Hot Water Heaters to Solar Water Heating Systems in Single-Family Houses—Model Validation and Optimization

Bernardo

2013

Energies

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“…Then, the model is applied to forecast the daily DHW volume over one year with a daily update of data. The output of our model is compared to estimations given by the daily average consumption and by the PG model [7] which is a moving average on the same day of the week during the last two months. Figures 6 to 9 represent the forecasts and error distribution of the ARMA model for three residences.…”

Section: Resultsmentioning

confidence: 99%

“…However, such models require strong computational time and detailed information about the residents like their number, age or social profile that is often not available. Other methods are based on probability models [4] or moving average models [7]; nevertheless, they need strong assumptions of the DHW load profiles which do not take into account isolated fluctuations of the consumption.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Modeling for Real Domestic Hot Water Consumption Forecasting

2015

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In this paper, we study the real domestic hot water (DHW) consumptions from single family houses equipped with solar hot water tank. We model it to understand and forecast the daily needs of inhabitants. Thus, the forecasts can be integrated in a control strategy to optimize the energy cost by heating only the necessary DHW volume. At first, we realize a data analysis from real uses of several dwellings to lay assumptions of the statistical model. This study highlights a weekly periodicity, random fluctuations and the different profiles of consumption following the residence, the season, and the day of the week. Otherwise, having no prior information like the location or the number of residents, we propose an adaptive time series model which does not require strong a priori and computational time. Then, we develop an ARMA model to forecast the daily DHW volume and we apply it on each individual installation. This model allows to take into account the periodicity of one week, the consumption of the previous days and random fluctuations. The results obtained on real data show that this approach is very promising.

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“…The paper considers eight residences, 30-min resolution and a 12-week model training period. The proposed autoregressive moving average (ARMA) model is compared to the (a) benchmark mean model and (b) moving average on the same day of the week during the last two months [27]. It is concluded that the ARMA model gives higher precision and better recovery from large variations (holidays).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hot Water Consumption in Residential Houses

Gelažanskas

Gamage

2015

Energies

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An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one of the most energy-intensive devices, where its energy demand is shiftable in time without influencing the comfort level. Tailored hot water usage predictions and advanced control techniques could enable these devices to supply ancillary energy balancing services. The paper analyses a set of hot water consumption data from residential dwellings. This work is an important foundation for the development of a demand-side management strategy based on hot water consumption forecasting at the level of individual residential houses. Various forecasting models, such as exponential smoothing, seasonal autoregressive integrated moving average, seasonal decomposition and a combination of them, are fitted to test different prediction techniques. These models outperform the chosen benchmark models (mean, naive and seasonal naive) and show better performance measure values. The results suggest that seasonal decomposition of the time series plays the most significant part in the accuracy of forecasting.

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Advanced control strategy of a solar domestic hot water system with a segmented auxiliary heater

Cited by 29 publications

References 2 publications

Retrofitting Conventional Electric Domestic Hot Water Heaters to Solar Water Heating Systems in Single-Family Houses—Model Validation and Optimization

Retrofitting Conventional Electric Domestic Hot Water Heaters to Solar Water Heating Systems in Single-Family Houses—Model Validation and Optimization

Statistical Modeling for Real Domestic Hot Water Consumption Forecasting

Forecasting Hot Water Consumption in Residential Houses

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