A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms

Abstract

Energy consumption forecasting for buildings plays a significant role in building energy management, conservation and fault diagnosis. Owing to the ease of use and adaptability of optimal solution seeking, data-driven techniques have proved to be accurate and efficient tools in recent years. This study provides a comprehensive review on the existing data-driven approaches for building energy forecasting, such as regression models, artificial neural networks, support vector machines, fuzzy models, grey models, etc. On this basis, the paper puts emphasis to the discussion on evolutionary algorithms hybridized models that combine evolutionary algorithms with regular data-driven models to improve prediction accuracy and robustness. Various combinations of such hybrid models are classified and their characteristics are analyzed. Finally, a detailed discussion on the advantages and challenges of current predictive models is provided. Practical Application: Building energy consumption prediction is important for building energy management, efficiency and fault diagnosis. For existing buildings, multisourced, heterogeneous or inadequate data-driven models may lead to convergence problem or poor model accuracy. To this end, a state of art review on building energy forecasting technique is helpful for related professionals in the building industry.

Towards developing a systematic knowledge trend for building energy consumption prediction

“…Artificial intelligence:[9], [13], [32], [14], [21], [25]- [29], [31] Machine learning: Support vector machine [61], [116], [164] [167], [187], [189], [204] Outperforms other methods on linearly separable problems…”

“… Assumptions  The physically-based model may not be appropriate for all scenarios  Often requires detailed/exhaustive building information  Time-consuming and laborintensive Statistical methods： [8], [14], [16], [21], [26], [31], [32] Ordinary Least squares regression [41], Linear regression [37], [121], [163], Logistic regression [41], [101], Stepwise regression, Multivariate adaptive regression splines [128], Locally estimated scatterplot smoothing.…”

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

“…A method of introducing a weighting factor into the original data to obtain the predicted data. This belongs to the category of time series forecasting [8]. The basic idea of the exponential smoothing forecasting method is that the forecasted value is a weighted sum of historical data.…”

A reduced-dimension feature extraction method to represent retail store electricity profiles

“…There are extensive reviews on methods to predict, model and benchmark energy use in buildings [16,17,18,19,20,21]. A possible classification of the data-driven techniques used to predict energy demand [20,21] is: 1) conventional statistical techniques, 2) classification-based models, 3) support vector regression (SVR) model, 4) artificial neural networks (ANN), 5) genetic algorithms, 6) grey models, 7) fuzzy model and 8) other models (e.g. case-based reasoning).…”