Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China

Farzana, Shazia; Liu, Meng; Baldwin, Andrew; Hossain, Md. Uzzal

doi:10.1016/j.enbuild.2014.06.007

Cited by 75 publications

(29 citation statements)

References 12 publications

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“…a year). Previously, a common practice from both top down and bottom up approaches was to analyse electricity consumption data together with disconnected survey information [13,14]. As a consequence, electricity and survey data were not always matched for individual households nor were the time frames of the data sets matching.…”

Section: Limitations and Gapsmentioning

confidence: 99%

“…In a Chinese study, energy simulations were conducted to predict the future electricity demand of urban residential buildings in Chongqing [14]. However, the study sampled primary household electricity demand by a structured questionnaire and subsequent energy intensities of various drivers were collected only from the literature concerning simulation of annual energy consumption.…”

Section: Bottom Upmentioning

confidence: 99%

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Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia

Fan

MacGill

Sproul

2015

Energy and Buildings

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Section: Limitations and Gapsmentioning

confidence: 99%

Section: Bottom Upmentioning

confidence: 99%

Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia

Fan

MacGill

Sproul

2015

Energy and Buildings

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“…Table 1 provides an overview of the advantages and disadvantages of the different categories of models discussed in this section. Common techniques for energy consumption forecasting include time series models [26], Exponential Smoothing [27], Linear Regression [28], Generalized Additive Models [29,30], and Functional Data Analysis [13]. Such classical methods, also referred to as non-machine learning methods, have been comprehensively studied in the literature, and a useful overview of their common attributes can be found in [31].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Robust ensemble learning framework for day-ahead forecasting of household based energy consumption

2018

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Smart energy management mandates a more decentralized energy infrastructure, entailing energy consumption information on a local level. Household-based energy consumption trends are becoming important to achieve reliable energy management for such local power systems. However, predicting energy consumption on a household level poses several challenges on technical and practical levels. The literature lacks studies addressing prediction of energy consumption on an individual household level. In order to provide a feasible solution, this paper presents a framework for predicting the average daily energy consumption of individual households. An ensemble method, utilizing information diversity, is proposed to predict the day-ahead average energy consumption. In order to further improve the generalization ability, a robust regression component is proposed in the ensemble integration. The use of such robust combiner has become possible due to the diversity parameters provided in the ensemble architecture. The proposed approach is applied to a case study in France. The results show significant improvement in the generalization ability as well as alleviation of several unstable-prediction problems, existing in other models. The results also provide insights on the ability of the suggested ensemble model to produce improved prediction performance with limited data, showing the validity of the ensemble learning identity in the proposed model. We demonstrate the conceptual benefit of ensemble learning, emphasizing on the requirement of diversity within datasets, given to sub-ensembles, rather than the common misconception of data availability requirement for improved prediction.

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“…Multiple regression model for fast prediction of heating energy demand with application on residential multifamily building is done in [4]. Prediction of annual energy use for office building from heating and cooling perspective is investigated in [10]. Hence, it can be concluded that the regression algorithm is widely used due to its simplicity and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Analysis of energy signatures and planning of heating and domestic hot water energy use in buildings in Norway

et al. 2019

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Widespread introduction of low energy buildings (LEBs), passive houses, and zero emission buildings (ZEBs) are national target in Norway. In order to achieve better energy performance in these types of buildings and successfully integrate them in energy system, reliable planning and prediction techniques for heat energy use are required. However, the issue of energy planning in LEBs currently remains challenging for district heating companies. This article proposed an improved methodology for planning and analysis of domestic hot water and heating energy use in LEBs based on energy signature method. The methodology was tested on a passive school in Oslo, Norway. In order to divide energy signature curve on temperature dependent and independent parts, it was proposed to use piecewise regression. Each of these parts were analyzed separately. The problem of dealing with outliers and selection of the factors that had impact of energy was considered. For temperature dependent part, the different methods of modelling were compared by statistical criteria. The investigation showed that linear multiple regression model resulted in better accuracy in the prediction than SVM, PLS, and LASSO models. In order to explain temperature independent part of energy signature the hourly profiles of energy use were developed.

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Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China

Cited by 75 publications

References 12 publications

Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia

Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia

Robust ensemble learning framework for day-ahead forecasting of household based energy consumption

Analysis of energy signatures and planning of heating and domestic hot water energy use in buildings in Norway

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