Using regression analysis to predict the future energy consumption of a supermarket in the UK

Braun, Meike; Altan, Haşim; Beck, Steven

doi:10.1016/j.apenergy.2014.05.062

Cited by 220 publications

(104 citation statements)

References 15 publications

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“…Many bottom-up regression models for energy demand modeling rely on the Princeton Scorekeeping Method [25], which describes the fundamental correlation between outdoor temperature and heating energy consumption. Recent studies using regression for modeling and predicting energy consumption often analyze hourly or sub-hourly energy meter data [26][27][28][29][30][31][32][33].…”

Section: Methods For Modeling Aggregate Hourly Energy Consumptionmentioning

confidence: 99%

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Kipping

Trømborg

2017

Energies

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Sound estimates of future heat and electricity demand with high temporal and spatial resolution are needed for energy system planning, grid design, and evaluating demand-side management options and polices on regional and national levels. In this study, smart meter data on electricity consumption in buildings are combined with cross-sectional building information to model hourly electricity consumption within the household and service sectors on a regional basis in Norway. The same modeling approach is applied to model aggregate hourly district heat consumption in three different consumer groups located in Oslo. A comparison of modeled and metered hourly energy consumption shows that hourly variations and aggregate consumption per county and year are reproduced well by the models. However, for some smaller regions, modeled annual electricity consumption is over-or underestimated by more than 20%. Our results indicate that the presented method is useful for modeling the current and future hourly energy consumption of a regional building stock, but that larger and more detailed training datasets are required to improve the models, and more detailed building stock statistics on regional level are needed to generate useful estimates on aggregate regional energy consumption.

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Section: Methods For Modeling Aggregate Hourly Energy Consumptionmentioning

confidence: 99%

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Kipping

Trømborg

2017

Energies

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“…Sustainability 2017, 9, 0127 10.3390/su10010127 7 of 22 accepted minimum R 2 value of 0.7 specified in modelling work [38], it can be suggested that the kinetic relationship represented by the linear plot is sufficient to predict the yield of FAs generated during subcritical hydrolysis and can be employed to achieve the aims of this paper. This implies therefore that the kinetic parameters (Ahy and Ehy), can be easily estimated by plotting Ln khy, which is obtained from the graph slopes in Figure 4 against 1/T such that the slope and the intercept will give the Ehy/R and the Ahy respectively.…”

Section: Kinetic Modelling Of Subcritical Lipid Hydrolysis and Supercmentioning

confidence: 99%

Catalyst-Free Biodiesel Production Methods: A Comparative Technical and Environmental Evaluation

2018

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Abstract:In response to existing global focus on improved biodiesel production methods via highly efficient catalyst-free high temperature and high pressure technologies, this study considered the comparative study of catalyst-free technologies for biodiesel production as an important research area. In this study, therefore, catalyst-free integrated subcritical lipid hydrolysis and supercritical esterification and catalyst-free one step supercritical transesterification processes for biodiesel production have been evaluated via undertaking straight forward comparative energetic and environmental assessments. Energetic comparisons were undertaken after heat integration was performed since energy reduction has favourable effects on the environmental performance of chemical processes. The study confirmed that both processes are capable of producing biodiesel of high purity with catalyst-free integrated subcritical lipid hydrolysis and supercritical esterification characterised by a greater energy cost than catalyst-free one step supercritical transesterification processes for an equivalent biodiesel productivity potential. It was demonstrated that a one-step supercritical transesterification for biodiesel production presents an energetically more favourable catalyst-free biodiesel production pathway compared to the integrated subcritical lipid hydrolysis and supercritical esterification biodiesel production process. The one-step supercritical transesterification for biodiesel production was also shown to present an improved environmental performance compared to the integrated subcritical lipid hydrolysis and supercritical esterification biodiesel production process. This is because of the higher potential environment impact calculated for the integrated subcritical lipid hydrolysis and supercritical esterification compared to the potential environment impact calculated for the supercritical transesterification process, when all material and energy flows are considered. Finally the major contributors to the environmental outcomes of both processes were also clearly elucidated.

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“…Different types of mathematical models have been used in the past to estimate the space heating/cooling energy use of buildings. Statistical approaches such as regression [13][14][15], Artificial Neural Networks [16], and Support Vector Machines [17] are found in the literature for energy predictions. A combined physical and statistical approach has been used in [18].…”

Section: The Test Buildingmentioning

confidence: 99%

Comparison of Space Heating Energy Consumption of Residential Buildings Based on Traditional and Model-Based Techniques

Perera

Skeie

2017

Buildings

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This paper presents a comparison of different scenarios in controlling the space heating systems in residential buildings. The space heating energy consumption of a three-storey residential building is estimated using traditional control methods (fixed-temperature schedule and fixed-time schedule) and a mathematical model-based control strategy. The model-based control technique takes the usage pattern of the building into account and operates the heaters based on the calculated heating time of the building. The results from the experiments confirm that the use of a model in heating control is the best option, which can save up to 1400 kWh and 320 kWh per year compared to a fixed-temperature schedule and fixed-time schedule, respectively.

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Using regression analysis to predict the future energy consumption of a supermarket in the UK

Cited by 220 publications

References 15 publications

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Catalyst-Free Biodiesel Production Methods: A Comparative Technical and Environmental Evaluation

Comparison of Space Heating Energy Consumption of Residential Buildings Based on Traditional and Model-Based Techniques

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