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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: closed (13 January 2021) | Viewed by 29212

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Prof. Dr. Dirk Deschrijver

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Department of Information Technology, Ghent University, Technologiepark Zwijnaarde 126, 9052 Gent, Belgium
Interests: data analytics; machine learning; artificial intelligence; neural networks; surrogate modeling; simulation; optimization

Special Issue Information

Dear colleagues,

We are inviting submissions to a Special Issue of the Energies Journal on the subject area of “Improving Energy Efficiency through Data-Driven Modeling, Simulation, and Optimization”. Rising energy costs and the effect of greenhouse gases stimulate the need for more efficient energy systems. The increase of computational power combined with advanced modeling and simulation tools makes it possible to derive innovative solutions that can reduce the ecological footprint. This Special Issue focuses on novel contributions that are based on data-driven approaches, machine learning, and artificial intelligence for modeling, simulation, and optimization of energy systems.

Topics of interest for publication include, but are not limited to:

* Non-intrusive load monitoring of energy consumption in buildings;

* Data-driven modeling approaches for energy prediction and forecasting;

* Energy modeling using neural architectures and deep learning;

* Advanced decision making using machine learning or artificial intelligence;

* Energy flexibility assessment, demand prediction, and load balancing for smart grids;

* Reinforcement learning techniques for energy management and optimization;

* Simulation-based multiobjective optimization for energy and buildings;

* Optimization and advanced heuristics for intelligent or adaptive systems.

Prof. Dr. Dirk Deschrijver
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

load monitoring
energy forecasting
energy modeling
machine learning
smart intelligent systems
energy optimization
simulation

Published Papers (11 papers)

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Editorial

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3 pages, 154 KiB

Open AccessEditorial

Special Issue: “Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization”

by Dirk Deschrijver

Energies 2021, 14(6), 1543; https://doi.org/10.3390/en14061543 - 11 Mar 2021

Viewed by 1339

Abstract

In October 2014, EU leaders agreed upon three key targets for the year 2030: a reduction of at least 40% in greenhouse gas emissions, a saving of at least a 27% share for renewable energy, and at least a 27% improvement in energy [...] Read more.

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

Research

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22 pages, 7044 KiB

Open AccessArticle

An Innovative Technology for Monitoring the Distribution of Abutment Stress in Longwall Mining

by Zhibiao Guo, Weitao Li, Songyang Yin, Dongshan Yang and Zhibo Ma

Energies 2021, 14(2), 475; https://doi.org/10.3390/en14020475 - 18 Jan 2021

Cited by 6 | Viewed by 1800

Abstract

Fracturing roofs to maintain entry (FRME) is a novel longwall mining method, which has been widely used in China, leading a new mining revolution. In order to research the change law of side abutment pressure and movement law of overlying strata when using the FRME, a new abutment pressure monitoring device, namely, the flexible detection unit (FDU), is developed and is applied in the field. The monitoring results show that compared with the head entry (also called the non-splitting entry), the peak value of the lateral abutment pressure in the tail entry (also termed the splitting entry) is reduced by 17.2% on average, and the fluctuation degree becomes smaller. Then, finite difference software FLAC^3D is used to simulate the stress change of the solid coal on both sides of the panel. The simulation results show that the side abutment pressure of the tail entry decreases obviously, which is consistent with the measured results. Comprehensive analysis points out that after splitting and cutting the roof, the fissures can change the motion state of the overlying strata, causing the weight of the overburden borne by the solid coal to reduce; therefore, the side abutment pressure is mitigated. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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28 pages, 2781 KiB

Open AccessArticle

A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment

by Giovanni Gravito de Carvalho Chrysostomo, Marco Vinicius Bhering de Aguiar Vallim, Leilton Santos da Silva, Leandro A. Silva and Arnaldo Rabello de Aguiar Vallim Filho

Energies 2020, 13(22), 6014; https://doi.org/10.3390/en13226014 - 18 Nov 2020

Cited by 6 | Viewed by 2314

Abstract

This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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19 pages, 4537 KiB

Open AccessArticle

Investigation on the Mechanism of Heat Load Reduction for the Thermal Anti-Icing System

by Rongjia Li, Guangya Zhu and Dalin Zhang

Energies 2020, 13(22), 5911; https://doi.org/10.3390/en13225911 - 12 Nov 2020

Cited by 7 | Viewed by 1729

Abstract

The aircraft ice protection system that can guarantee flight safety consumes a part of the energy of the aircraft, which is necessary to be optimized. A study for the mechanism of the heat load reduction in the thermal anti-icing system under the evaporative mode was presented. Based on the relationship between the anti-icing heat load and the heating power distribution, an optimization method involved in the genetic algorithm was adopted to optimize the anti-icing heat load and obtain the optimal heating power distribution. An experiment carried out in an icing wind tunnel was conducted to validate the optimized results. The mechanism of the anti-icing heat load reduction was revealed by analyzing the influences of the key factors, such as the heating range, the surface temperature and the convective heat transfer coefficient. The results show that the reduction in the anti-icing heat load is actually the decrease in the convective heat load. In the evaporative mode, decreasing the heating range outside the water droplet impinging limit can reduce the convective heat load. Evaporating the runback water in the high-temperature region can lead to the less convective heat load. For the airfoil, the heating power distribution that has an opposite trend with the convective heat transfer coefficient can reduce the convective heat load. Thus, the optimal heating power distribution has such a trend that is low at the leading edge, high at the water droplet impinging limit and zero at the end of the protected area. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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17 pages, 2788 KiB

Open AccessArticle

Neural Methods Comparison for Prediction of Heating Energy Based on Few Hundreds Enhanced Buildings in Four Season’s Climate

by Tomasz Szul, Krzysztof Nęcka and Thomas G. Mathia

Energies 2020, 13(20), 5453; https://doi.org/10.3390/en13205453 - 19 Oct 2020

Cited by 22 | Viewed by 2175

Abstract

Sustainable development and the increasing demand for equitable energy use as well as the reduction of waste of energy are the author’s social and scientific motivations. This new paradigm is the selection of a pertinent methodology to evaluate the efficiency of habitat thermomodernization, which is one of the scientific tasks of the presented study. In order to meet the social and scientific requirements, 380 buildings from the end of the last century (made of large plate technology), which were thermally improved at the beginning of the XXI century, were designed for a comparative analysis of the predictive modelling of heating energy consumption. A specific set of important variables characterizing the examined buildings has been identified. Groups of variables were used to estimate the energy consumption in such a way as to achieve a compromise between the difficulty of obtaining them and the quality of forecast. To predict energy consumption, the six most appropriate neural methods were used: artificial neural networks (ANN), general regression trees (CART), exhaustive regression trees (CHAID), support regression trees (SRT), support vectors (SV), and method multivariant adaptive regression splines (MARS). The quality assessment of the developed models used the mean absolute percentage error (MAPE) also known as mean absolute percentage deviation (MAPD), as well as mean bias error (MBE), coefficient of variance of the root mean square error (CV RMSE) and coefficient of determination (R²), which are accepted as statistical calibration standards by (American Society of Heating, Refrigerating and Air-Conditioning Engineers) ASHRAE. On this basis, the most effective method has been chosen, which gives the best results and therefore allows to forecast with great precision the energy consumption (after thermal improvement) for this type of residential building. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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20 pages, 2995 KiB

Open AccessArticle

Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

by Chun-Wei Chen, Chun-Chang Li and Chen-Yu Lin

Energies 2020, 13(17), 4368; https://doi.org/10.3390/en13174368 - 24 Aug 2020

Cited by 8 | Viewed by 2120

Abstract

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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15 pages, 837 KiB

Open AccessArticle

Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

by Anthony Faustine and Lucas Pereira

Energies 2020, 13(13), 3374; https://doi.org/10.3390/en13133374 - 1 Jul 2020

Cited by 46 | Viewed by 4258

Abstract

Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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21 pages, 1128 KiB

Open AccessArticle

An Ant Colony Algorithm for Improving Energy Efficiency of Road Vehicles

by Alberto V. Donati, Jette Krause, Christian Thiel, Ben White and Nikolas Hill

Energies 2020, 13(11), 2850; https://doi.org/10.3390/en13112850 - 3 Jun 2020

Cited by 3 | Viewed by 1900

Abstract

The number and interdependency of vehicle CO₂ reduction technologies, which can be employed to reduce greenhouse emissions for regulatory compliance in the European Union and other countries, has increasingly grown in the recent years. This paper proposes a method to optimally combine these technologies on cars or other road vehicles to improve their energy efficiency. The methodological difficulty is in the fact that these technologies have incompatibilities between them. Moreover, two conflicting objective functions are considered and have to be optimized to obtain Pareto optimal solutions: the CO₂ reduction versus costs. For this NP-complete combinatorial problem, a method based on a metaheuristic with Ant Colony Optimization (ACO) combined with a Local Search (LS) algorithm is proposed and generalized as the Technology Packaging Problem (TPP). It consists in finding, from a given set of technologies (each with a specific cost and CO₂ reduction potential), among all their possible combinations, the Pareto front composed by those configurations having the minimal total costs and maximum total CO₂ reduction. We compare the performance of the proposed method with a Genetic Algorithm (GA) showing the improvements achieved. Thanks to the increased computational efficiency, this technique has been deployed to solve thousands of optimization instances generated by the availability of these technologies by year, type of powertrain, segment, drive cycle, cost type and scenario (i.e., more or less optimistic technology cost for projected data) and inclusion of off-cycle technologies. The total combinations of all these parameters give rise to thousands of distinct instances to be solved and optimized. Computational tests are also presented to show the effectiveness of this new approach. The outputs have been used as basis to assess the costs of complying with different levels of new vehicle CO₂ standards, from the perspective of different manufacturer types as well as vehicle users in Europe. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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13 pages, 3974 KiB

Open AccessArticle

Data-Driven Modeling of Fuel Consumption for Turboprop-Powered Civil Airliners

by Benoit G. Marinus and Antoine Hauglustaine

Energies 2020, 13(7), 1695; https://doi.org/10.3390/en13071695 - 3 Apr 2020

Cited by 2 | Viewed by 2209

Abstract

Next to empirical correlations for the specific range, fuel flow rate, and specific fuel consumption, a response surface model for estimates of the fuel consumption in early design stages is presented and validated. The response-surface’s coefficients are themselves predicted from empirical correlations based solely on the operating empty weight. The model and correlations are all derived from fuel consumption data of nine current civil turbo-propeller aircraft and are validated on a separate set. The model can accurately predict fuel weights of new designs for any combination of payload and range within the current range of efficiency of the propulsion. The accuracy of the model makes it suited for preliminary and conceptual design of near-in-kind turbo-propeller aircraft. The model can shorten the design cycle by delivering fast and accurate fuel weight estimates from the first design iteration once the operating empty weight is known. Since it is based solely on the operating empty weight and it is accurate, the model is a sound variant to the Breguet range equation in order to make accurate fuel weight estimates. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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19 pages, 7484 KiB

Open AccessArticle

Heat Transfer and Energy Consumption of Passive House in a Severely Cold Area: Simulation Analyses

by Fang Wang, Wen-Jia Yang and Wei-Feng Sun

Energies 2020, 13(3), 626; https://doi.org/10.3390/en13030626 - 2 Feb 2020

Cited by 15 | Viewed by 4090

Abstract

In order to improve the heat transfer in enclosure structure of passive houses in cold area with complex climatic conditions, a three-dimensional model is established to investigate the time-by-case changes of outdoor temperature and solar irradiation based on the principle of integral change and the method of response coefficient and harmonious wave reaction. The variations of hourly cooling and heating loads with outdoor temperature and solar irradiation are analyzed. As simulated by cloud computing technology, the passive building energy consumption meets the requirements of passive building specifications. In the present research, super-thermal insulation external wall, enclosure structure of energy-conserving doors and windows, and high efficiency heat recovery system are employed to achieve a constant temperature without active mechanical heating and cooling, which suggests a strategic routine to remarkably decrease the total energy consumption and annual operation cost of passive building. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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Review

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24 pages, 364 KiB

Open AccessReview

A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

by Jessica Walther and Matthias Weigold

Energies 2021, 14(4), 968; https://doi.org/10.3390/en14040968 - 12 Feb 2021

Cited by 38 | Viewed by 4234

Abstract

In the context of the European Green Deal, the manufacturing industry faces environmental challenges due to its high demand for electrical energy. Thus, measures for improving the energy efficiency or flexibility are applied to address this problem in the manufacturing industry. In order to quantify energy efficiency or flexibility potentials, it is often necessary to predict or forecast the energy consumption. This paper presents a systematic review of state-of-the-art of existing approaches to predict or forecast the energy consumption in the manufacturing industry. Seventy-two articles are classified according to the defined categories System Boundary, Modelling Technique, Modelling Focus, Modelling Horizon, Modelling Perspective, Modelling Purpose and Model Output. Based on the reviewed articles future research activities are derived. Full article

(This article belongs to the Special Issue Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization)

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