A Novel Hybrid Approach of KPCA and SVM for Building Cooling Load Prediction

Li, Xuemei; Ding, Lixing; Jinhu, Lv; Xu, Gang; Li, Jibin

doi:10.1109/wkdd.2010.137

Cited by 27 publications

(11 citation statements)

References 6 publications

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“…ANN models have been applied mainly for fast estimation of heating and cooling loads [4], energy consumption and efficiency [5,6] and optimization and control of heating systems [7,8]. In the building sector, SVM have been mainly used for classification of energy usage [9], cooling and heating loads forecasting [10] and prediction of energy consumption [11]. Other ML and soft computing techniques, as for example genetic algorithms (GA) or random forest (RF), have also been used in this area to a lesser extent [12,13].…”

Section: Introductionmentioning

confidence: 99%

Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches

Nebot

Mugica

2020

Applied Sciences

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The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications.

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Section: Introductionmentioning

confidence: 99%

Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches

Nebot

Mugica

2020

Applied Sciences

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“…These methods include learning algorithms such as kernel transformed Support Vector Regression (SVR), Random Forests (RF) and the Autoregressive Integrated Moving Average (ARIMA) [16,18,19,27,41,[44][45][46]. Support Vector Regression is a conventional non-parametric regression method, which utlizes different kernels and support vectors to enable the ability to approximate complex model.…”

Section: Benchmark Methodsmentioning

confidence: 99%

“…However, neural networks require significant amount of training data to produce such accurate results [34,36] and hence are not always suited for building load prediction, particularly in newly constructed buildings, where there is not much historical data available. Other algorithms, such as Support Vector Regression [18,19,38,39,43,45], Random Forests [41,42] and Autoregressive models [44,46] have also been used to develop models for building load prediction. They have been used for commercial building load forecasting and for short-term and day-ahead load predictions.…”

Section: Related Workmentioning

confidence: 99%

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Prakash

Rajagopal

et al. 2018

Energies

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Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short-and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).

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“…There are many physical and data-driven modeling techniques available that can be used to model the energy consumption of buildings (Bourdeau et al, 2019). Data-driven models emerge as the most suitable option for the BECE analysis rather than classical physics-based modeling (Li et al, 2010). Moreover, the recent researchers employed detailed information from the IFC model for BECE analysis.…”

Section: Introductionmentioning

confidence: 99%

Room-Based Energy Demand Classification of Bim Data Using Graph Supervised Learning

Kiavarz

Jadidi

Rajabifard

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in Building Energy Consumption Estimation (BECE). Despite extensive research, BIM is less used in BECE data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.

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A Novel Hybrid Approach of KPCA and SVM for Building Cooling Load Prediction

Cited by 27 publications

References 6 publications

Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches

Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Room-Based Energy Demand Classification of Bim Data Using Graph Supervised Learning

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