1,720,970 research outputs found
A model to simulate the energy consumption of the Italian residential building stock with regional resolution
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Analysis and application of a lumped-capacitance model for urban building energy modelling
No full textBuildings are one of the major responsible of energy consumption and carbon emissions worldwide. Due to the continuous growth of cities, researchers moved their focus from single buildings to urban scale analyses. The present work aims at demonstrating the reliability of a lumped-capacitance model in the evaluation of heating and cooling demand at urban level. The model presented extends a previous one with different modules for solar radiation pre-processing, HVAC systems and photovoltaic production estimation. A case-study district of 13 buildings in Padua (Italy) has been analysed, considering detailed single buildings simulations with EnergyPlus as benchmark. Results show that the model leads to good accuracy in the evaluation of the district energy demand for both space heating and cooling. The major sources of error are geometrical simplifications, shadowing and thermal zoning effects, thus showing the importance of high-quality input data in urban modelling. However, results highlight how simple corrections may enhance the accuracy when detailed geometrical information is not available. Finally, the model has been used to assess the cost and energy saving potential of several building retrofit measures and PV installations on the considered district, providing an important indication in the investment priority for decision makers
Lumped-capacitance models to evaluate the urban cooling energy consumptions: analysis on a case-study district in the University of Padua
No full textThe growth of urban population and consumption has boosted research about Urban Building Energy Modelling (UBEM). This paper proposes an analysis of the cooling energy demand of a real district with a new UBEM platform, EUReCA (Energy Urban Resistance Capacitance Approach). EUReCA consists of a Python-based tool to predict cities' energy demand based on simplified physical models. The article compares the hourly temperature and humidity profiles to those calculated with EnergyPlus, used as a benchmark. Moreover, hourly and seasonal cooling demand are verified and peak load difference is highlighted. Finally, the effect of several retrofit actions on building envelopes and HVAC plants is discussed and compared to the current scenario
Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses
No full textResearch effort in analyzing the energy consumption of buildings in districts or cities is increasing as new paradigms of distributed energy production and sharing are spreading. In this work, the Urban Building Energy Model of the Quad, an area of the University of California Davis campus, is presented, validated, and analyzed for possible actions to reduce the district cooling energy consumption. Energy conservation measures involve buildings' air handling units and district chilled water generation. To investigate this system, a district cooling networks module has been developed in EUReCA, the Urban Building Energy Modeling tool used for the analyses. The model has been validated with energy demand and temperature data from 2018 to 2020, resulting in a district cooling demand deviation lower than 7% for 2018 and 2019. Normalized Root Mean Square Error is lower than 35% for each building, proving the reliability at the hourly time scale. Systems energy reduction actions like heat recovery units' installation and heuristic control of the air supply cooling temperature are beneficial in reducing the cooling demand, and they allow a more efficient discharging of the chilled water storage, reducing the average electricity price by load shifting during peak demand
A comparison between grey-box models and neural networks for indoor air temperature prediction in buildings
Full text linkModel Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air temperature prediction tests have been performed on simulated and measured data from buildings with different thermal insulation and thermal mass during both heating and cooling seasons. Results show that neural networks have, on average, a better prediction performance than grey-box models. Both modelling approaches are affected by the building characteristics and by the season considered. The grey-box models require less training data, although the latter seems to play a role only in the worse-performing tests. When user setpoint changes in the testing phase, the LSTM neural network shows a significant drop in the root mean square error. In conclusion, although LSTM outperforms grey-box models on average, the reduced training data and higher reliability under normal operating conditions, as well as their linearity, make RC models a strong alternative for predictive controllers
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Assessment of the urban heat island impact on building energy performance at district level with the EUReCA platform
No full textIn recent decades, the cooling energy demand in urban areas is increasing ever faster due to the global warming and the growth of developing economies. In this perspective, the urban building energy modelling community is focusing its research activities on innovative tools and policy actions to improve cities’ sustainability. This work aims to present a novel module of the EUReCA (Energy Urban Resistance Capacitance Approach) platform for evaluating the effects of the interaction between district’s buildings in the cooling season. EUReCA predicts the urban energy demand using a bottom-up approach and low computational resources. The new module allows us to evaluate the mutual shading between buildings and the urban heat island effects, and it is well integrated with the calculation of the energy demand of buildings. The analysis was carried out considering a real case study in Padua (Italy). Results show that the urban heat island causes an average increase of 2.2 °C in the external air temperature mainly caused by the waste heat rejected from cooling systems. This involves an increase in urban cooling energy and electricity demand, which can be affected between 6 and 8%. The latter is the most affected by the urban heat island (UHI), due to the degradation it causes on the HVAC systems’ efficiency
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