scholarly journals Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1273 ◽  
Author(s):  
Antonio Attanasio ◽  
Marco Piscitelli ◽  
Silvia Chiusano ◽  
Alfonso Capozzoli ◽  
Tania Cerquitelli
Keyword(s):  
Decision Tree ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Primary Energy ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Estimation Model ◽  
Primary Energy Demand

Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

scholarly journals Nearly Zero Energy Standard for Non-Residential Buildings with High Energy Demands—An Empirical Case Study Using the State Related Properties of Bavaria

2017 ◽  
Author(s):  
Michael Keltsch ◽  
Werner Lang ◽  
Thomas Auer
Keyword(s):  
Energy Demand ◽  
Energy Savings ◽  
Residential Buildings ◽  
Energy Performance ◽  
High Energy ◽  
Primary Energy ◽  
The State ◽  
Zero Energy ◽  
Primary Energy Demand ◽  
New Buildings

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

Predicting Peak Energy Demand for an Office Building Using Artificial Intelligence (AI) Approaches

2021 ◽  
Author(s):  
Yuxuan Chen ◽  
Patrick Phelan
Keyword(s):  
Artificial Intelligence ◽  
Energy Demand ◽  
Prediction Models ◽  
Meteorological Data ◽  
Building Energy ◽  
Energy Performance ◽  
Training Data ◽  
Office Building ◽  
Support Vector ◽  
Peak Energy

Abstract Due to the technological advancement in smart buildings and the smart grid, there is increasing desire of managing energy demand in buildings to achieve energy efficiency. In this context, building energy prediction has become an essential approach for measuring building energy performance, assessing energy system efficiency, and developing energy management strategies. In this study, two artificial intelligence techniques (i.e., ANN = artificial neural networks and SVR = support vector regression) are examined and used to predict the peak energy demand to estimate the energy usage for an office building on a university campus based on meteorological and historical energy data. Two-year energy and meteorological data are used, with one year for training and the following year for testing. To investigate the seasonal load trend and the prediction capabilities of the two approaches, two experiments are conducted relying on different scales of training data. In total, 10 prediction models are built, with 8 models implemented on seasonal training datasets and 2 models employed using year-round training data. It is observed that a backpropagation neural network (BPNN) performs better than SVR when dealing with more data, leading to stable generalization and low prediction error. When dealing with less data, it is found that there is no dominance of one approach over another.

scholarly journals Analysis of the Effects of Strengthening Building Energy Policy on Multifamily Residential Buildings in South Korea

2020 ◽  
Vol 12 (9) ◽  
pp. 3566
Author(s):  
Byung Chang Kwag ◽  
Sanghee Han ◽  
Gil Tae Kim ◽  
Beobjeon Kim ◽  
Jong Yeob Kim
Keyword(s):  
Energy Consumption ◽  
South Korea ◽  
Energy Policy ◽  
Energy Demand ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Primary Energy ◽  
Building Envelope ◽  
Primary Energy Consumption

The purposes of this study were to overview the building-energy policy and regulations in South Korea to achieve energy-efficient multifamily residential buildings and analyze the effects of strengthening the building design requirements on their energy performances. The building energy demand intensity showed a linear relationship with the area-weighted average U-values of the building envelope. However, improving the thermal properties of the building envelope was limited to reducing the building-energy demand intensity. In this study, the effects of various energy conservation measures (ECMs) on the building-energy performance were compared. Among the various ECMs, improving the boiler efficiency was found to be the most efficient measure for reducing the building-energy consumption in comparison to other ECMs, whereas the building envelope showed the least impact, because the current U-values are low. However, in terms of the primary energy consumption, the most efficient ECM was the lighting power density because of the different energy sources used by various ECMs and the different conversion factors used to calculate the primary energy consumption based on the source type. This study showed a direction for updating the building-energy policy and regulations, as well as the potential of implementing ECMs, to improve the energy performances of Korean multifamily residential buildings.

scholarly journals Nearly Zero Energy Standard for Non-Residential Buildings with High Energy Demands—An Empirical Case Study Using the State Related Properties of Bavaria

2017 ◽  
Author(s):  
Michael Keltsch ◽  
Werner Lang ◽  
Thomas Auer
Keyword(s):  
Energy Demand ◽  
Energy Savings ◽  
Residential Buildings ◽  
Energy Performance ◽  
High Energy ◽  
Primary Energy ◽  
The State ◽  
Zero Energy ◽  
Primary Energy Demand ◽  
New Buildings

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

scholarly journals THE INFLUENCE OF SCHEDULES OF OPEN OFFICE OCCUPANTS’ PRESENCE ON BUILDING’S ENERGY DEMAND

2020 ◽  
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė
Keyword(s):  
Energy Efficient ◽  
Energy Demand ◽  
Energy Performance ◽  
Primary Energy ◽  
Simulation Software ◽  
Design Stage ◽  
Sustainable Buildings ◽  
Standard Case ◽  
Primary Energy Demand ◽  
Performance Calculation

Many studies show, that there is a difference between actual and design energy consumption in energy efficient and sustainable buildings. As a rule, buildings consume more energy than it has been foreseen at the design stage. Occupants’ behaviour in buildings is also identified as one of the main reasons causing the so called Performance Gap. Having mobile workstations, opened plan offices are becoming more popular in design solutions in sustainable buildings. Here we have studied one of such office spaces. Monitoring of real occupancy was performed and real occupation schedules were statistically generated. The schedules were compared to the ones given by European Standard for energy performance calculation as well as with default schedules proposed by simulation software DesignBuilder. The comparison shows a significantly lower measured occupancy compared to the above-mentioned schedules. To compare the influence of occupancy related assumptions on predicted energy demand, DesignBuilder model was created and simulated for 3 different occupancy schedules. The results have shown that primary energy demand of a building due to assumptions related with an occupancy, compared to default DesignBuilder schedules are: 111 kWh/m² (32%) higher than the standard case and 152 kWh/m² (44%) than the actual one.

scholarly journals Feasibility Study on Energy Audit and Data Driven Analysis Procedure for Building Energy Efficiency: Bench-Marking in Korean Hospital Buildings

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 3006 ◽  
Author(s):  
Hwang ◽  
Cho ◽  
Moon
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Flow ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Public Hospitals ◽  
Data Driven ◽  
Building Energy Efficiency ◽  
Energy Audit

Growths in population, increasing demand for health care services and comfort levels, together with patients on the rise in time spent inside hospitals, assure the upward trend that energy demand will continue in the future. Since the hospital buildings operate 24 hours, 365 days a year for the treatment and restoration of patients, they are approximately 2–3 times more energy-intensive than normal buildings. For this reason, energy efficiency in hospitals is one of the prime objectives for energy policy at regional, national and international levels. This study aims to find how meaningful energy performance, reflecting good energy management and energy conservation measures (ECMs), can be operated for hospital buildings, a category encompassing complex buildings with different systems and large gaps between them. Energy audit allows us to obtain knowledge from the healthcare facility, in order to define and tune data driven analysis rules. The use of benchmarking in the energy audit of healthcare facilities enables immediate comparison between hospitals. Data driven energy analysis also allows ascertaining their expected energy consumption and estimating the possible savings margin by using the building energy flow chart. In the 2015–2017 periods, bench-marking of four public hospitals in Seoul were audited for the energy consumption related to weather conditions, total area, bed numbers, employee numbers, and analyzed for building energy flow by zones, energy sources, systems and equipment. This is a practice-based learning in a hospital project. The results reveal that the average annual energy consumption of a hospital under normal conditions, and energy efficiency factors are divided into energy baselines, energy consumption goals for energy saving and energy usage trends for setting ECMs, respectively. The indicator dependent on the area of inpatients (number of beds) proved to be the most suitable as a reference to quantify the energy consumption of a hospital.

Energy Performance of SOFC Cogeneration System for Residential Buildings in Chinese Cold Areas

2014 ◽  
Vol 935 ◽  
pp. 48-51
Author(s):  
Xin Zhi Gong ◽  
Yasunori Akashi ◽  
Daisuke Sumiyoshi
Keyword(s):  
Thermal Energy ◽  
Energy Efficient ◽  
Energy Demand ◽  
Residential Buildings ◽  
Reduction Rate ◽  
Energy Performance ◽  
Primary Energy ◽  
Beijing City ◽  
Primary Energy Demand ◽  
Cogeneration System

Primary energy reduction and energy efficiency improvement are important targets to be achieved in every society and in residential buildings in particular. An energy-efficient and low-emissions solid oxide fuel cell (SOFC) cogeneration system is a promising electric and thermal energy generation technology for implementation in future residential buildings. This paper aims to analyze the energy performance in terms of primary energy demand and its reduction rate when SOFC cogeneration system is used in residential buildings. This study outlines SOFC cogeneration system and its simulation method, and then develops a standard family model for simulation under cold weather condition in China and selected Beijing city as an example, and finally compares them with traditional power and heat generation system based on gas and electricity. The results show that SOFC cogeneration system is an energy-efficient alternative power and thermal energy cogeneration technology for cold climatic cities such as Beijing, and can offer a large reduction rate (about 15.8% in winter) of primary energy demand in residential buildings. This study also finds that the significant reductions in primary energy demand of SOFC system result for the periods with air temperature decreasing.

scholarly journals A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1347 ◽  
Author(s):  
Wenting Zhao ◽  
Juanjuan Zhao ◽  
Xilong Yao ◽  
Zhixin Jin ◽  
Pan Wang
Keyword(s):  
Energy Demand ◽  
Pso Algorithm ◽  
Primary Energy ◽  
Small Samples ◽  
Support Vector ◽  
Energy Structure ◽  
Ensemble Model ◽  
Primary Energy Demand ◽  
Energy Demands ◽  
Development Plans

Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

scholarly journals Investigation of the BNB Building Certification for the Further Development of the Energy Performance Certificate

2021 ◽  
Vol 2069 (1) ◽  
pp. 012233
Author(s):  
Manuela Walsdorf-Maul ◽  
Laura Dommack ◽  
Michael Schneider
Keyword(s):  
Life Cycle ◽  
Energy Demand ◽  
Energy Performance ◽  
Primary Energy ◽  
Assessment System ◽  
Technical Quality ◽  
Primary Energy Demand ◽  
German Speaking ◽  
Environmentally Sound ◽  
Further Development

Abstract In this study, a life cycle oriented planning of buildings is proposed to support future building developers and planners in making environmentally sound decisions on the basis of comprehensive information. The study, in which the building certification BNB (Bewertungssystem Nachhaltiges Bauen, or “Assessment System for Sustainable Building”) is carried out on the example of an office building, is applicable to German-speaking countries. In addition to meeting the requirements of the 2020 German Energy Act for Buildings (GebäudeEnergieGesetz, GEG), the aim is to optimize the building with regard to sustainability criteria of the BNB by revising and expanding the existing planning so that the “gold” quality label can eventually be achieved. The biggest influence on this optimization process is, among other things, the life cycle costs, the adaptability of the building, the primary energy demand as well as the technical quality. Based on these findings, this research paper details the further development of the energy performance certificate, before in a final step the building assessment can be graphically presented with regard to both aspects – energy efficiency (final energy) and sustainability (in terms of ecological, economic, socio-cultural, functional and technical quality, process quality and location characteristics) – from the production phase through the usage phase up to the disposal phase.

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