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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 June 2023) | Viewed by 13133

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Prof. Dr. Zhe Tian

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Guest Editor

School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Interests: building integrated energy system; operation optimization of HVAC system; building and urban climate

Dr. Yan Ding

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Guest Editor

School of Environment Science and Technology, Tianjin Key Laboratory of Built Environment and Energy Application, Tianjin University, Tianjin 300350, China
Interests: intelligent building; building energy system optimization; building occupant behavior; building load prediction; integrated energy utilization
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Special Issue Information

Dear Colleagues,

Against the macro background of energy saving and carbon reduction, traditional building energy system planning and design methods and operation optimization technology are facing new challenges. On the one hand, the large-scale application of renewable energy in buildings has made "multi-energy complementing" and "comprehensive energy" the new normal of building energy, which has not only increased the difficulty of system planning and design, but also intensified the complexity of planning and design methods because of the uncertainty of renewable energy. On the other hand, with the addition of "economy", "energy saving", "low carbon emission" and "flexibility" operation objectives, the optimization of building energy system operation becomes capable of non-traditional methods.

In order to better promote the exchange and discussion of building energy planning and operation technology among scholars in related fields, Energies invites Prof. Zhe Tian and Associate Prof. Yan Ding from Tianjin University to launch a Special Issue titled "Building Energy System Planning and Operation".

Prof. Dr. Zhe Tian
Dr. Yan Ding
Guest Editors

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Published Papers (6 papers)

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Research

19 pages, 3738 KiB

Open AccessArticle

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

by Jiale Tang, Kuixing Liu, Weijie You, Xinyu Zhang and Tuomi Zhang

Energies 2023, 16(14), 5570; https://doi.org/10.3390/en16145570 - 24 Jul 2023

Cited by 1 | Viewed by 1548

Abstract

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. Full article

(This article belongs to the Special Issue Building Energy System Planning and Operation)

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19 pages, 3963 KiB

Open AccessArticle

Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study

by Haiyan Meng, Yakai Lu, Zhe Tian, Xiangbei Jiang, Zhongqing Han and Jide Niu

Energies 2023, 16(14), 5402; https://doi.org/10.3390/en16145402 - 15 Jul 2023

Viewed by 1054

Abstract

Many researchers are devoted to improving the prediction accuracy of daily load profiles, so as to optimize day-ahead operation strategies to achieve the most efficient operation of district heating and cooling (DHC) systems; however, studies on load prediction and operation strategy optimization are generally isolated, which leaves the following question: what day-head load prediction performance should be paid attention to in the operation optimization of DHC systems? In order to explain this issue, and taking an actual DHC system as a case study, this paper proposes an evaluation method for the prediction of daily cooling load profiles by considering the impact of inaccurate prediction on the operation of a DHC system. The evaluation results show the following: (1) When prediction models for daily load profiles are developed, the prediction accuracy of the daily mean load should be emphasized, and there is no need to painstakingly increase the accuracy of load profile shapes. (2) CV and RMSE are the most suitable deviation measures (compared to others, e.g., MAPE, MAE, etc.) for the evaluation of load prediction models. A prediction model with 27.8% deviation (CV) only causes a 3.74% deviation in operation costs; thus, the prediction performance is enough to meet the engineering requirements for the DHC system in this paper. Full article

(This article belongs to the Special Issue Building Energy System Planning and Operation)

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20 pages, 3200 KiB

Open AccessArticle

A Policy Roadmap for the Energy Renovation of the Residential and Educational Building Stock in Italy

by Gianluca Ruggieri, Francesca Andreolli and Paolo Zangheri

Energies 2023, 16(3), 1319; https://doi.org/10.3390/en16031319 - 26 Jan 2023

Cited by 12 | Viewed by 3241

Abstract

The building sector is crucial in all of the possible net zero scenarios suggested for the European Union. In this area, the Italian situation is exemplary. Italy suffers from an aging and low-performance building stock and needs to increase its annual rate of energy retrofits in order to achieve its 2030 and 2050 targets. Even though since at least 2007, several different incentives schemes intended to stimulate energy-efficiency interventions have been in place, Italy has not been sufficiently able to promote deep retrofits. In 2020, in order to help the economy recover after the lockdowns that were introduced to face the first phase of the COVID-19 pandemic, the existing incentives were increased to up to 110% of investments for interventions that improved the energy class by at least two grades. This so-called “Superbonus” was also extended to the public social housing sector thanks to a credit assignment scheme. Given the results of this provisional phase, a possible policy roadmap for the energy renovation of the residential and educational building stock in Italy is presented in this paper through an analysis of data related to the implementation of current instruments in terms of number of interventions, investment needed, energy savings and evaluation of potential benefits and costs that can derive from an increase in the current deep-renovation rate. Through definition of a long-term renovation strategy, this paper illustrates how market barriers and other issues in instrument design can be tackled and how policymakers can help to develop a sustainable long-term roadmap for energy-efficient buildings. Beyond the residential sector, public buildings, particularly educational buildings, are taken into consideration as well, as they are places of collective use that represent the social values of fairness and sustainability and can therefore have an exemplary role for private initiatives. Full article

(This article belongs to the Special Issue Building Energy System Planning and Operation)

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15 pages, 3387 KiB

Open AccessArticle

Demand-Response-Oriented Load Aggregation Scheduling Optimization Strategy for Inverter Air Conditioner

by Qifen Li, Yihan Zhao, Yongwen Yang, Liting Zhang and Chen Ju

Energies 2023, 16(1), 337; https://doi.org/10.3390/en16010337 - 28 Dec 2022

Cited by 8 | Viewed by 2207

Abstract

In recent years, the peak–valley differences in urban power loads have been increasing. It is difficult to maintain the real-time balance of a power system by relying solely on the generation-side resources. As a typical flexible load, an air conditioning load can balance the supply and demand of a power grid by adjusting power using the thermal inertia of buildings. From the perspective of a load aggregator, this study models and aggregates the dispatch of a single inverter air conditioner distributed in a region to determine the adjustment potential of an air conditioning cluster. Then, according to the demand response capacity requirements, an optimal strategy for the aggregate dispatch of an inverter air conditioner considering incentive compensation measures is proposed with the objective of maximizing the load quotient economic benefit. The sensitivity analysis of the compensation factor for temperature rise is also performed. The results show that 3000 inverter air conditioners in the load quotient dispatch area participate in the demand response for 4 h, with a load reduction of 1.267 MW and a net income of RMB 14,435.97. Secondly, an increase in the temperature rise compensation factor will reduce the cost of temperature rise compensation by the loader to the user, but it will also reduce the load reduction and the net income of the loader. This study has practical significance for load aggregators to formulate compensation strategies and improve the economic benefits of participating in demand response. Full article

(This article belongs to the Special Issue Building Energy System Planning and Operation)

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23 pages, 8346 KiB

Open AccessArticle

Residential Building Construction Techniques and the Potential for Energy Efficiency in Central Asia: Example from High-Altitude Rural Settlement in Kyrgyzstan

by Kedar Mehta, Wilfried Zörner and Rick Greenough

Energies 2022, 15(23), 8869; https://doi.org/10.3390/en15238869 - 24 Nov 2022

Cited by 3 | Viewed by 2326

Abstract

Building construction in rural Kyrgyzstan is heavily dominated by earthen buildings. Old and inappropriate residential building structures contribute significantly to high domestic space heating energy consumption. Therefore, it is necessary to understand the relevant building construction techniques. However, the scant information on Kyrgyz building techniques, especially for high-altitude rural settlements, was the prime motivation to perform the presented study. The key objective of the study is to investigate residential building construction techniques in high-altitude rural Kyrgyzstan, and this was to be achieved by house visits during field trips, literature review, and pilot interviews with local people. The analysis enabled the detailed identification of individual building envelopes as well as predominant building materials to be recorded. Based on the assessment, a housing profile was created that represents the typical characteristics of traditional rural Kyrgyz houses. Furthermore, the study demonstrates the potential for energy savings in rural Kyrgyz houses of 50–70%. However, local conditions prevent people from making improvements to all domestic energy efficiency parameters simultaneously. Therefore, the study developed a ‘sequential roadmap’ to reduce domestic space heating demand in different phases based on simulation studies. Existing low-income rural Kyrgyz habitations can use the presented roadmap to reduce domestic space heating demand sequentially to overcome financial barriers and, therefore, contribute to establishing sustainable buildings in Kyrgyzstan. These results may be partially replicated in other Central Asian rural communities depending on their location and building characteristics. Full article

(This article belongs to the Special Issue Building Energy System Planning and Operation)

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31 pages, 11240 KiB

Open AccessArticle

Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior

by Yan Ding, Xiao Pan, Wanyue Chen, Zhe Tian, Zhiyao Wang and Qing He

Energies 2022, 15(22), 8689; https://doi.org/10.3390/en15228689 - 19 Nov 2022

Cited by 4 | Viewed by 1754

Abstract

Traditional building energy consumption prediction methods lack the description of occupant behaviors. The interactions between occupants and equipment have great influence on building energy consumption, which cause a large deviation between the predicted results and the actual situation. To address this problem, a two-part prediction model, consisting of a basic part related to the building area and a variable part related to stochastic occupant behaviors, is proposed in this study. The wavelet decomposition and reconstruction method is firstly used to split the energy consumption. A relationship between the low frequency energy consumption data and the building area is discovered, and an area-based index is used to fit the basic part of the prediction model. With a quantitative description of the occupant–equipment interaction by classifying the equipment into environmentally relevant and environmentally irrelevant equipment, an agent-based model is established in the variable part. According to the validation given by two case office buildings, the prediction error can be controlled to 2.8% and 10.1%, respectively, for the total and the hourly building energy consumption. Compared to the prediction method which does not consider occupant–equipment interactions, the proposed model can improve prediction accuracy by 55.8%. Full article

(This article belongs to the Special Issue Building Energy System Planning and Operation)

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