A review of the-state-of-the-art in data-driven approaches for building energy prediction

Sun, Ying; Haghighat, Fariborz; Fung, Benjamin C. M.

doi:10.1016/j.enbuild.2020.110022

Cited by 288 publications

(101 citation statements)

References 113 publications

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“…The data alone is not enough. And a small amount of clean and reliable data can be more valuable than a huge amount of raw data that has not been cleaned or treated [13], [15].…”

Section: A Collection and Accessing The Datamentioning

confidence: 99%

Data Driven: An Overview and Practical Measures for Organizations

Oliveira¹,

Braga²

2021

IJAERS

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Data is considered a primary resource for innovation. The existence of a large amount of available data, as well as technological tools capable of explore them, allows companies to extract information that can be used to create and implement new ideas and new projects. To this end, the details regarding the care that organizations should have with data are explored. The difficulties regarding the adoption of datadriven approach and some measures to implement this type of decisionmaking approach are also discussed. Some examples of data-driven approaches for diverse industries and products are shown. A real example of prediction model for decision making that is based on industrial data is also discussed. This example shows the difficulties in the preparation of data for the building of these models, which confirms that most of the time spent in the construction of predictive models it is due to this step. The use of the data-driven approach allows organizations to obtain superior results in their processes, thus becoming a tremendous competitive advantage and a special strategic factor in a highly competitive market, regardless of the field of activity.

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“…The data alone is not enough. And a small amount of clean and reliable data can be more valuable than a huge amount of raw data that has not been cleaned or treated [13], [15].…”

Section: A Collection and Accessing The Datamentioning

confidence: 99%

Data Driven: An Overview and Practical Measures for Organizations

Oliveira¹,

Braga²

2021

IJAERS

View full text Add to dashboard Cite

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“…[3]. The implementation of artificial intelligence models, has shown to be very effective in building energy use predictions and classification, while the recent reviews can be found in [3] and [4]. By using these techniques, the modelling of building energy behaviour comes down to defining relationship between variables based on the significant amount of high quality historical data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid artificial intelligence model for prediction of heating energy use

et al. 2022

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Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study, we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are hard to define and quantify. For heating energy use modelling, complex relationship between the input and output variables is not strictly linear nor non-linear. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using different statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple Linear Regression (MLR) was selected for the linear modelling, while the non-linear part was predicted using Feedforward (FFNN) and Radial Basis (RBFN) neural network. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that the hybrid FFNN model and the hybrid RBFN model achieved better results than each of the individual FFNN and RBFN neural networks and MLR on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.

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“…Machine learning (ML) methods can be applied to define surrogate models -black-box models that aim to replace physical models. They are fitted with existing data, usually collected from simulations, measurements, or databases [8], but also energy audits [9].…”

Section: Introductionmentioning

confidence: 99%

Classification of retrofit measures for residential buildings according to the global cost

2021

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Data-driven black-box surrogate models are widely used in research related to buildings energy efficiency. They are based on machine learning techniques, learn from available data, and act as a replacement for or an addition to complex and computationally intensive knowledge-based models. Surrogate models can predict energy demand, indoor air temperature, or occupants behavior; explore search space in optimization problems; learn control rules; etc. This paper analyzes surrogate models that classify building retrofit measures directly according to the global cost. In addition, they quantify the importance of each variable for the classification process. The models are based on random forest classifiers, which are fast and powerful ensemble learners. They can be applied to effectively reduce search spaces when optimizing energy renovation measures or to rapidly identify projects that deserve financial support. This approach is applied to two residential buildings and three scenarios of price development. The training process uses a small share of retrofit options assessed with standard calculations of the heating and cooling demands, as well as the global cost. The results show very high classification performance, even when the models are trained with small and imbalanced training sets. The obtained recall, precision, and F-score values are mostly above 95%, except for extremely small training sets.

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A review of the-state-of-the-art in data-driven approaches for building energy prediction

Cited by 288 publications

References 113 publications

Data Driven: An Overview and Practical Measures for Organizations

Data Driven: An Overview and Practical Measures for Organizations

Hybrid artificial intelligence model for prediction of heating energy use

Classification of retrofit measures for residential buildings according to the global cost

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