Multiple regression models to predict the annual energy consumption in the Spanish banking sector

Aranda, Alfonso; Ferreira, Germán; Mainar-Toledo, María Dolores; Scarpellini, Sabina; Sastresa, Eva Llera

doi:10.1016/j.enbuild.2012.02.040

Cited by 110 publications

(49 citation statements)

References 19 publications

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“…Regression analysis is also held up as a suitable method for providing results in a format that are relatively simple to interpret for non-statistical audiences, for example local authority officers (Bianco et al 2009;Aranda et al 2012). The use of a regression model is a compromise between the simplicity of the evaluation method and the accuracy of the result without requiring a significant amount of input data (Aranda et al 2012). Tso and Yau's (2007) study comparing regression analysis, decision tree and neural networks for predicting electricity consumption found the difference in error between the three methods was minimal.…”

Section: Statistical Modellingmentioning

confidence: 99%

Benchmarking and tracking domestic gas and electricity consumption at the local authority level

et al. 2015

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Government, local authority and industry initiatives to improve the energy efficiency of housing stocks are central to national and international commitments to reduce carbon dioxide emissions. To be effective, initiatives need to target homes which, given their location, size, fuel type and occupancy, use more energy than expected. This paper illustrates how energy efficiency benchmarks can be developed that account for these factors and highlights the shortcomings of relying on simple energy consumption statistics. The study uses existing data (with national coverage) and the measured electricity and gas consumption of groups consisting of, on average, 500-700 households to benchmark and track domestic gas and electricity consumption across England. Multiple regression models, which account for 65 % of the variation in domestic gas consumption and 73 % of domestic electricity variation, are used to derive the benchmarks. The actual gas and electricity consumption of each group of homes is compared against the derived benchmark and an energy efficiency index presented. The approach enables changes in energy efficiency to be tracked temporally, for example to assess the effectiveness of government, local authority or industry initiatives. National and city-scale patterns of energy efficiency are also discussed.

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Section: Statistical Modellingmentioning

confidence: 99%

Benchmarking and tracking domestic gas and electricity consumption at the local authority level

et al. 2015

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“…Aranda et al [10] used linear regression models to predict the annual energy consumption in the Spanish banking sector. The energy consumption of a single building was predicted as a function of its construction characteristics, climatic area and energy performance.…”

Section: State Of the Artmentioning

confidence: 99%

Methodology for Commercial Buildings Thermal Loads Predictive Models Based on Simulation Performance

Kapetanakis¹,

Mangina²,

Finn³

2015

Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques

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Commercial buildings incorporate Building Energy Management Systems (BEMS) to monitor indoor environment conditions as well as controlling Heating Ventilation and Air Conditioning (HVAC) systems. Measurements of temperature, humidity and energy consumption are typically stored within BEMS. These measurements include underlying information regarding building thermal response, which is crucial for the calculation of heating and cooling loads. Forecasting of building thermal loads can be achieved using data records from BEMS. Accurate predictions can be produced when introducing these data records to datamining predictive models. Incomplete datasets are often acquired when extracting data from the BEMS; hence detailed representations of commercial buildings can be implemented using EnergyPlus. For the purposes of the research described in this paper, different types of commercial buildings in various climates are examined to investigate the scalability of the predictive models.

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“…Regression methods tend to be simple and easily applicable to any type of building [8]. At present, the main forms of regression analysis applied are multiple linear regressive (MLR) models [9], auto regressive (AR) models , and autoregressive with exogenous inputs (ARX) models [ 10 ] have been used as building cooling load prediction models. Compared with other categories, although current regression analysis in literature is considered to be less accuracy, it is practical and simple to implement for real applications in building cooling load prediction.…”

Section: Introductionmentioning

confidence: 99%

An improved ARX model for hourly cooling load prediction of office buildings in different climates

2018

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Abstract. An attempt was made to develop an improved autoregressive with exogenous (ARX) model for office buildings cooling load prediction in five major climates of China. The cooling load prediction methods can be arranged into three categories: regression analysis, energy simulation, and artificial intelligence. Among them, the regression analysis methods using regression models are much simple and practical for real applications. However, traditional regression models are often helpless to manage multiparameter dynamic changes, making it not accurate as the other two categories. Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity. To improve the prediction accuracy of regression analysis methods, higher order and interaction terms are included in improved ARX based on traditional ARX model. The improved ARX model consists of eight variables, with eleven coefficients accessed at a time. For applications and evaluations, an office building in major cities within each climatic zone was selected as a representation. These cities were Harbin, Beijing, Nanjing, Kunming and Guangzhou respectively. The coefficient of determination R 2 is greater than 0.9 in five cities. The prediction results show that the improved ARX model can adapt to different climatic conditions, including those nonlinearity cases.

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Multiple regression models to predict the annual energy consumption in the Spanish banking sector

Cited by 110 publications

References 19 publications

Benchmarking and tracking domestic gas and electricity consumption at the local authority level

Benchmarking and tracking domestic gas and electricity consumption at the local authority level

Methodology for Commercial Buildings Thermal Loads Predictive Models Based on Simulation Performance

An improved ARX model for hourly cooling load prediction of office buildings in different climates

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