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Energies
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Forecasting and Optimization of Energy Use in Buildings
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Forecasting and Optimization of Energy Use in Buildings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 12011

Special Issue Editor

Prof. Dr. Jui-Sheng (Rayson) Chou

E-Mail Website
Guest Editor
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Interests: civil & hydraulic engineering informatics; project quantitative analytics for sustainable engineering and the built environment; decision, risk, failure analysis & disaster management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy efficiency in buildings has been attracted great attention from researchers and practitioners over the decades. The energy sector is central to highly-topical issues and serves as a key policy instrument addressing major societal needs. This Special Issue of “Forecasting and Optimization of Energy use in Buildings” provides a forum for information on innovation, research, development and demonstration in the areas of energy conversion and utilization, the optimal use of energy resources, analysis and optimization of energy processes and sustainable energy systems.

This Special Issue solicits original papers, review articles, case studies, and new technology analyses that present new research results in energy and buildings. Moreover, cutting-edge research and lessons-learned from formal analyses of case studies, supported by conceptual, theoretical or ‘proof of concept’ frameworks are welcome. We wish to attract a broad spectrum of themes, including concepts and approaches which not only focus on energy management in buildings, but also consider the wider impact on subsequent change and development needed.

Finally, this Special Issue expects to provide a platform for academicians, researchers, and engineers to share their experience and new thinking as well as innovative approaches and solutions to any problems in various areas for efficient energy management toward sustainable development in buildings. It is timely to bring together the collective thinking in this area. Anticipated themes include, but are not limited to:

  • Applied decision making with energy data
  • Artificial intelligence/machine learning/deep learning/reinforcement learning for energy use in buildings
  • Building energy model calibration
  • Building energy modelling & management
  • Building energy performance and simulation
  • Building information modelling for energy analysis
  • Building life cycle assessment
  • Energy demand and consumption forecasting
  • Intelligent monitoring system for energy use
  • Life cycle assessment and energy simulation
  • Net-zero energy building
  • Optimization of energy efficiency
  • Renewable energy sources in buildings
  • Smart infrastructure for energy and buildings
  • Sustainable and green buildings
  • Sustainable energy and operations

Prof. Dr. Jui-Sheng Chou
Guest Editor

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

  • Artificial intelligence
  • Building energy modeling
  • Energy use
  • Forecast analysis and simulation
  • Energy optimization
  • Policy instrument
  • Smart and green building

Published Papers (4 papers)

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Research

30 pages, 2012 KiB  
Article
Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology
by Benedetto Grillone, Gerard Mor, Stoyan Danov, Jordi Cipriano, Florencia Lazzari and Andreas Sumper
Energies 2021, 14(17), 5556; https://doi.org/10.3390/en14175556 - 06 Sep 2021
Cited by 8 | Viewed by 3081
Abstract
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been [...] Read more.
Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities. Full article
(This article belongs to the Special Issue Forecasting and Optimization of Energy Use in Buildings)
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19 pages, 674 KiB  
Article
Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters
by Diogo M. F. Izidio, Paulo S. G. de Mattos Neto, Luciano Barbosa, João F. L. de Oliveira, Manoel Henrique da Nóbrega Marinho and Guilherme Ferretti Rissi
Energies 2021, 14(7), 1794; https://doi.org/10.3390/en14071794 - 24 Mar 2021
Cited by 18 | Viewed by 2752
Abstract
The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. [...] Read more.
The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE). Full article
(This article belongs to the Special Issue Forecasting and Optimization of Energy Use in Buildings)
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18 pages, 4781 KiB  
Article
Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast
by Jin Sol Hwang, Ismi Rosyiana Fitri, Jung-Su Kim and Hwachang Song
Energies 2020, 13(21), 5633; https://doi.org/10.3390/en13215633 - 28 Oct 2020
Cited by 22 | Viewed by 3070
Abstract
This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load [...] Read more.
This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime. Full article
(This article belongs to the Special Issue Forecasting and Optimization of Energy Use in Buildings)
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10 pages, 496 KiB  
Article
Replacement and Maintenance Decision Analysis for Hydraulic Machinery Facilities at Reservoirs under Imperfect Maintenance
by Sou-Sen Leu and Tao-Ming Ying
Energies 2020, 13(10), 2507; https://doi.org/10.3390/en13102507 - 15 May 2020
Cited by 4 | Viewed by 2212
Abstract
After the long-term operation of reservoir facilities, they will become nonoperational due to the material deterioration and the performance degradation. One of crucial decisions is to determine the maintenance or replacement of the facilities in a cost-effective manner. Conventional replacement models seldom consider [...] Read more.
After the long-term operation of reservoir facilities, they will become nonoperational due to the material deterioration and the performance degradation. One of crucial decisions is to determine the maintenance or replacement of the facilities in a cost-effective manner. Conventional replacement models seldom consider the maintenance effect. The facilities after maintenance are generally not as good as new, but are relatively restored. The target of this study is to establish a replacement decision model of the reservoir facilities under imperfect maintenance. By combining the theories of reliability analysis, imperfect maintenance, and engineering economics, the best timing of replacement that achieves cost-effectiveness is analyzed and proposed. Lastly, based on the design of experiments (DOE) and simulation, the regression curve chart for the economical replacement decision is established. Once the failure rate, the age of recovery after maintenance, and the ratio of maintenance cost to replacement cost are estimated based on historical data, the cost-effective replacement time of hydraulic machinery facilities will be efficiently determined. Full article
(This article belongs to the Special Issue Forecasting and Optimization of Energy Use in Buildings)
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