Seasonal autoregressive modelling of water and fuel consumptions in buildings

Lowry, Gordon; Bianeyin, Felix U.; Shah, Nirav

doi:10.1016/j.apenergy.2006.02.003

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…ARIMAX models have been used in building-related applications, including modelling of water and fuel use [Lowry et al, 2007], and forecasting and controlling the peak demand for electricity [Hoffman, 1998]. Herter & Wayland [2010] used a limited form of this method, with auto-regressive lag 1 terms only, in an analysis ofthe effect of pricing regimes on peak household electricity use.…”

Section: Methods 4: Time-series Regressionmentioning

confidence: 99%

A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use

2011

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Energy Policy, 39, (10), pp. 6376-6389, DOI: 10.1016/j.enpol.2011 http://www.nrc-cnrc.gc.ca/ircThe material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42 AbstractWe analysed the peak load reductions due to a residential direct load control program for air-conditioners in one jurisdiction in southern Ontario in 2008. In this program, participant thermostats were increased by 2°C for four hours on five event days (when systemwide capacity was expected to be strained). We used hourly, whole-house data for 195 load control participant households and 268 non-participant households, and four different methods of analysis ranging from simple spreadsheet-based comparisons of average loads on event days, to complex time-series regression. Average peak load reductions were 0.2-0.9 kW per household, or 10-35%. However, there were large differences (up to a factor of four) between event days and across event hours, and in results for the same event day/hour with different analysis methods. There was also a wide range of load reductions between individual households. Policy makers would be 1 '" wise to consider multiple analysis methods when making decisions regarding which demand-side management programs to support, and how they might be incentivized .Further investigation of what type of households contribute most to aggregate load reductions would also help policy makers better target programs.

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Section: Methods 4: Time-series Regressionmentioning

confidence: 99%

A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use

2011

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“…Thus the power variable included lighting, office equipment, lab equipment, and other plug loads that were likely directly related to occupancy, and thus perhaps of more relevance to the goals of this study 2 (in [11] the authors suggested that non-weather related energy use would benefit from a separate analysis). Figure 1 shows the average hourly values of this power variable.…”

Section: Energy Use Forecasts Using Arimaxmentioning

confidence: 99%

“…The most general mathematical form of the ARIMAX model equation is as follows [8] is the time delay for the effect of the ith predictor (if the predictor cannot affect the dependent variable for a certain number of time steps for basic physical reasons) ARIMAX models have been applied to building-related applications, including: modelling and forecasting of room temperature [9,10], modelling of water and fuel use in a variety of buildings [11], optimizing the operation of cold storage in a large building [12], and forecasting and controlling the peak demand for electricity at a government complex [4].…”

Section: Introductionmentioning

confidence: 99%

Building-level occupancy data to improve ARIMA-based electricity use forecasts

Newsham

Birt

2010

Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building

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The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42 benjamin.birt@nrc-cnrc.gc.ca ABSTRACTThe energy use of an office building is likely to correlate with the number of occupants, and thus knowing occupancy levels should improve energy use forecasts.To gather data related to total building occupancy, wireless sensors were installed in a three-storey building in eastern Ontario, Canada comprising laboratories and 81 individual work spaces. Contact closure sensors were placed on various doors, PIR motion sensors were placed in the main corridor on each floor, and a carbon-dioxide sensor was positioned in a circulation area. In addition, we collected data on the number of people who had logged in to the network on each day, network activity, electrical energy use (total building, and chilling plant only), and outdoor temperature. We developed an ARIMAX model to forecast the power demand of the building in which a measure of building occupancy was a significant independent variable and increased the model accuracy. The results are promising, and suggest that further work on a larger and more typical office building would be beneficial. If building operators have a tool that can accurately forecast the energy use of their building several hours ahead they can better respond to utility price signals, and play a fuller role in the coming Smart Grid.

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“…Furthermore, the investment and the annual energy expenditures related to the components were also discussed [15]. Various modeling approaches were examined in describing the energy for hot water consumption records in residential buildings [16,17]. However, the energy savings potential for localized waste heat recovery systems in high-rise residential buildings has not been deeply developed [18].…”

Section: Introductionmentioning

confidence: 99%

Shower water heat recovery in high-rise residential buildings of Hong Kong

2010

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This paper investigates the potential for shower water heat recovery from bathrooms equipped with instantaneous water heaters in high-rise residential buildings of Hong Kong. A simple single-pass counter-flow heat exchanger installed horizontally beneath the shower drain is employed as a localized heat recovery measure for preheating cold water going to a water heater. The thermal energy exchange is evaluated using the effectiveness-number of 32 transfer units (ε-NTU) approach. Shower usage patterns including shower operating time and 33 water flow rate sampled from an interview survey via the Monte Carlo sampling technique, 34 together with the water temperatures at the shower heads, shower drains and cold water 35 supply mains recorded in sample shower operations, are the input parameters. The results 36 indicate that 4-15% shower water heat can be recovered through a 1.5 m long single-pass 37 counter-flow heat exchanger for a drainage pipe of diameter 50 mm. 38

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Seasonal autoregressive modelling of water and fuel consumptions in buildings

Cited by 11 publications

References 12 publications

A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use

A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use

Building-level occupancy data to improve ARIMA-based electricity use forecasts

Shower water heat recovery in high-rise residential buildings of Hong Kong

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