nZEB: bridging the gap between design forecast and actual performance data

Aste, N.; Adhikari, R.S.; Buzzetti, Michela; Pero, Claudio Del; Huerto-Cardenas, Harold Enrique; Leonforte, Fabrizio; Miglioli, Alessandro

doi:10.1016/j.enbenv.2020.10.001

Cited by 15 publications

(9 citation statements)

References 36 publications

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“…[4] focused instead on how the climate change is influencing the performance of NZEBs; similarly, [5] found how current climate trends, in the short term analysis, are responsible of higher energy requirement for space cooling, rather than space heating -and will continue to do so. [6] discuss the gap between forecast and actual consumption, and how this is fundamental in improving the energy efficiency on a wider scale.…”

Section: Introductionmentioning

confidence: 99%

Towards Zero Energy Buildings: The UniZEB case study

Carnieletto,

Mitrovic,

Riccardi

et al. 2024

E3S Web Conf.

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Buildings are among the primary contributors to global energy consumption, and reducing their demand is one of the challenges that need to be considered for a sustainable future. Zero Energy Buildings (ZEB) represent one of the most promising strategies in this endeavor, and in this sense, the UniZEB project addresses this issue with an innovative approach. It is a Zero Energy Building Laboratory of the University of Padova, built up from a collaboration between local companies, students, and researchers, featuring high performance HVAC and envelope technologies integrated with renewable energy sources. The present work offers an overview of the project, showing some of the already-faced challenges, as well as future opportunities of research and study. The purpose of the laboratory is indeed to offer the students the possibility to put in practice the knowledge they gain through university, as well as exploring new subjects through recent research topics, e.g., the development of a sensor network for the monitoring of the building, the dynamic model calibration, able to compare expected and current energy demand. The paper aims also to demonstrate the potential of a project like UniZEB, proving how research, innovation, and collaboration can shape the future of sustainable construction.

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Section: Introductionmentioning

confidence: 99%

Towards Zero Energy Buildings: The UniZEB case study

Carnieletto,

Mitrovic,

Riccardi

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

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“…The importance of the energy model is key to demonstrate that if the monthly balance is considered, sometimes the size of the PV plant does not fit the owner's financial criteria. However, it is not only the energy model that is important; weather forecasting errors can also seriously distort the expected results, leading to significant errors, such as "different energy distributions between heating and cooling" [11].…”

Section: Introductionmentioning

confidence: 99%

Weather Files for the Calibration of Building Energy Models

González

Ruiz

et al. 2022

Applied Sciences

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In the fight against climate change, energy modeling is a key tool used to analyze the performance of proposed energy conservation measures for buildings. Studies on the integration of photovoltaic energy in buildings must use calibrated building energy models, as only with them is the demand curve real, and the savings obtained at the self-consumption level, energy storage in the building, or feed into the grid are accurate. The adjustment process of a calibrated model depends on aspects inherent to the building properties (envelope parameters, internal loads, use schedules) as well as external to them (weather, ground properties, etc.). Naturally, the uncertainty of each is essential to obtaining good results. As for the meteorological data, it is preferable to use data from a weather station located in the building or its surroundings, although this is not always possible due to the cost of the initial investment and its maintenance. As a result, weather stations with public access to their data, such as those located at airports or specific locations in cities, are largely used to perform calibrations of building energy models, making it challenging to converge the simulated model with measured data. This research sheds light on how this obstacle can be overcome by using weather data provided by a third-party company, bridging the gap between reality and energy models. For this purpose, calibrations of the two buildings proposed in Annex 58 were performed with different weather configurations, using the mean absolute error (MAE) uncertainty index and Spearman’s rank correlation coefficient (rho) as comparative measures. An optimal and cost-effective solution was found as an alternative to an on-site weather station, based on the use of a single outdoor temperature sensor in combination with third-party weather data, achieving a robust and reliable building energy model.

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“…Smart cities, smart grids, and smart buildings [ 1 , 2 , 3 ], demand response and demand side management [ 4 , 5 ], renewable energy sources and their integration [ 6 ], nearly zero energy buildings [ 7 ], model predictive control [ 8 ], electric vehicles [ 9 ], etc., are perhaps some of the research areas that have received more emphasis in recent years. Global warming due to climate change, the scarcity of natural resources, and the growing global increase in energy demand are some of the reasons for this search for greater efficiency and smart energy use.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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nZEB: bridging the gap between design forecast and actual performance data

Cited by 15 publications

References 36 publications

Towards Zero Energy Buildings: The UniZEB case study

Towards Zero Energy Buildings: The UniZEB case study

Weather Files for the Calibration of Building Energy Models

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

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