Data Mining and Machine Learning in Building Energy Analysis

Magoulès, Frédéric; Zhao, Hai‐Xiang

doi:10.1002/9781118577691

Cited by 31 publications

(27 citation statements)

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“…On the other hand the data-driven models were further classified by ASHRAE [23] in three broad groups, highlighted in Figure 5, that have also been adopted in the following classifications [56][57][58][59][60]:…”

Section: Modeling the Building And The Hvac Systemmentioning

confidence: 99%

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Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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Section: Modeling the Building And The Hvac Systemmentioning

confidence: 99%

“…Calibrated simulation models: that are high fidelity response models based on physical principle to calculate thermal dynamics and energy behavior of whole building level or for sublevel components [59]. The approach is the same as the one mentioned in the forward approach, but in this case the models are calibrated using real data gathered on field.…”

mentioning

confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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“…Among the several methods available to analyze data and to build the energy baseline, we chose statistical regression [25,27,81,82] as the most effective to organize the data collection accordingly. However, physical models, artificial neural networks or even machine learning techniques [15,28] are employable. First of all, it is important to highlight that the choice of the method mainly depends on the following factors: (i) available resources for the control system ramp-up; (ii) available resources for the control system maintenance; (iii) boundaries and chosen level of detail (i.e.…”

Section: Methodology For Real Time Energy Performance Controlmentioning

confidence: 99%

“…Furthermore, from a maintenance point of view, the use of advanced techniques can support the real time performance control during production, implementing faults detection [12][13][14][15], fault diagnosis [16,17] and remaining useful life estimation to support the optimization of manufacturing management and maintenance scheduling [18].…”

Section: Introductionmentioning

confidence: 99%

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

et al. 2019

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Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.

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“…where t j is a bias for the unit j, and f is the activation function which can be commonly defined as the sigmoid function [21]:…”

Section: Annmentioning

confidence: 99%

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Zhang

Zhu

et al. 2019

Energies

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Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.Energies 2019, 12, 1680 2 of 17 usage of resources based on accurate predictions. Accurate natural gas price forecasting not only provides an important guide for effective implementation of energy policy and planning, but also is extremely significant in economic planning, energy investment, and environmental conservation. Therefore, researchers continue to study natural gas price forecasting models with great interest, with the aim of making predictions as accurate as possible in future.There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. A great number of researchers have investigated natural gas price prediction with the aid of various machine learning methods so far. For instance, Abrishami and Varahrami mixed the data handling neural network technique with a rule-based expert system in forecasting natural gas prices [3]. Busse et al. used a nonlinear autoregressive exogenous model neural network [4]. Azadeh et al. studied a hybrid neuro-fuzzy method composed of ANN, fuzzy linear regression, and conventional regression [5]. Salehnia et al. developed several nonlinear models using the gamma test, including local linear regression, dynamic local linear regression, and ANN models [6]. Ceperic et al. proposed a strategic seasonality-adjusted, support vector regression machine-based model [7]. Su et al. utilized a least squares regression boosting algorithm in natural gas price prediction [8].As indicated in the abovementioned existing studies that exploited machine learning tools for natural gas price prediction, ANN and SVM are widely used machine learning methods in forecasting natural gas prices. In addition to ANN and SVM, this study will introduce two other common machine learning approaches, GBM and GPR (these two methods were used for forecasting hourly loads in US [9]). ...

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Data Mining and Machine Learning in Building Energy Analysis

Cited by 31 publications

References 0 publications

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

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