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Building energy modeling (BEM) is used to support (nearly) zero-energy building (ZEB) projects, since this kind of software represents the only available option to forecast building energy consumption with high accuracy. BEM may also be used during preliminary analyses or feasibility studies, but simulation results are usually too detailed for this stage of the project. Aside from that, when optimization algorithms are used, the implied high number of energy simulations causes very long calculation times. Therefore, designers could be discouraged from the extensive use of BEM to conduct optimization analyses. Thus, they prefer to study and compare a very limited amount of acknowledged alternative designs. In relation to this problem, the scope of the present study is to obtain an easy-to-use tool to quickly forecast the energy consumption of a building with no direct use of BEM to support fast comparative analyses at the early stages of energy projects. In response, a set of automatic energy assessment tools was developed based on machine learning techniques. The forecasting tools are artificial neural networks (ANNs) that are able to estimate the energy consumption automatically for any building, based on a limited amount of descriptive data of the property. The ANNs are developed for the Po Valley area in Italy as a pilot case study. The ANNs may be very useful to assess the energy demand for even a considerable number of buildings by comparing different design options, and they may help optimization analyses.
Marco Pittarello; Massimiliano Scarpa; Aurora Ruggeri; Laura Gabrielli; Luigi Schibuola. Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages. Applied Sciences 2021, 11, 5377 .
AMA StyleMarco Pittarello, Massimiliano Scarpa, Aurora Ruggeri, Laura Gabrielli, Luigi Schibuola. Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages. Applied Sciences. 2021; 11 (12):5377.
Chicago/Turabian StyleMarco Pittarello; Massimiliano Scarpa; Aurora Ruggeri; Laura Gabrielli; Luigi Schibuola. 2021. "Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages." Applied Sciences 11, no. 12: 5377.
The research about energy efficiency in buildings has exponentially increased during the last few years. Nevertheless, both research and practice still cannot rely on complete methodologies tailored for building portfolios as a whole, because the attention has always been drawn to individual premises. Yet, energy efficiency analyses need to go beyond the single building perspective and incorporate strategic district approaches to optimize the retrofit investment. For this purpose, several aspects should be considered simultaneously, and new methodologies should also be promoted. Therefore, this paper aims to discuss energy retrofit campaigns in building portfolios, drawing an exhaustive and updated review about the challenge of jumping from the single-building perspective to a stock-based analysis. This research discusses the publications available on the topic from five key aspects that are all essential steps in achieving a complete and reliable study of energy efficiency at a portfolio level. They are energy modelling and assessment, energy retrofit design, decision-making criteria assessment, optimal allocation of (financial) resources and risk valuation. This review, therefore, advocates for joint consideration of the problem as a basis on which to structure further disciplinary developments. Research gaps are highlighted, and new directions for future research are suggested.
Aurora Ruggeri; Laura Gabrielli; Massimiliano Scarpa. Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects. Sustainability 2020, 12, 7465 .
AMA StyleAurora Ruggeri, Laura Gabrielli, Massimiliano Scarpa. Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects. Sustainability. 2020; 12 (18):7465.
Chicago/Turabian StyleAurora Ruggeri; Laura Gabrielli; Massimiliano Scarpa. 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects." Sustainability 12, no. 18: 7465.
Tables of performance of installed HVAC (Heating, Ventilation and Air Conditioning) devices are important in the development of consistent building energy audits and appropriate control strategies. However, given the possible complexity of HVAC devices and the need for the deployment to computational environments, tables of performance should be passed in a more complete and flexible format, compared with the current practices in the HVAC sector. In such a context, this paper describes the phases of development and application of Artificial Neural Networks (ANNs) aimed at the assessment of the performance of a Dedicated Outdoor Air System (DOAS). ANNs are well renowned because of their applications in many important fields such as autonomous driving systems, speech recognition, etc. However, they may be used also to calculate the output of complex phenomena (like the ones involved in HVAC components) and are characterized by a very flexible and comprehensive formulation which would be able to adapt to any HVAC component or system. In the frame of this study, three ANNs have been developed and tested, for the full description of the performance of a DOAS. The developed ANNs were trained by means of data coming from a proprietary software. The achieved ANNs showed robust and reliable behavior and ensure high accuracy (mean absolute errors usually below 0.1 K on temperatures and 0.3% on capacity and power) and flexibility. Moreover, in some cases, they may be used also for the identification of anomalous data present among the sets of training and validation data.
Marco Pittarello; Massimiliano Scarpa; Luigi Schibuola; Chiara Tambani. Application of artificial neural networks to the simulation of a Dedicated Outdoor Air System (DOAS). Energy Procedia 2018, 148, 146 -153.
AMA StyleMarco Pittarello, Massimiliano Scarpa, Luigi Schibuola, Chiara Tambani. Application of artificial neural networks to the simulation of a Dedicated Outdoor Air System (DOAS). Energy Procedia. 2018; 148 ():146-153.
Chicago/Turabian StyleMarco Pittarello; Massimiliano Scarpa; Luigi Schibuola; Chiara Tambani. 2018. "Application of artificial neural networks to the simulation of a Dedicated Outdoor Air System (DOAS)." Energy Procedia 148, no. : 146-153.
Luigi Schibuola; Massimiliano Scarpa; Chiara Tambani. Innovative technologies for energy retrofit of historic buildings: An experimental validation. Journal of Cultural Heritage 2018, 30, 147 -154.
AMA StyleLuigi Schibuola, Massimiliano Scarpa, Chiara Tambani. Innovative technologies for energy retrofit of historic buildings: An experimental validation. Journal of Cultural Heritage. 2018; 30 ():147-154.
Chicago/Turabian StyleLuigi Schibuola; Massimiliano Scarpa; Chiara Tambani. 2018. "Innovative technologies for energy retrofit of historic buildings: An experimental validation." Journal of Cultural Heritage 30, no. : 147-154.
This paper shows the results of a monitoring campaign on an invertible ground source heat pump (GSHP) with borehole heat exchangers installed in the historical center of Venice in the frame of the renovation of an ancient building where other renewable energy systems, such as solar energy systems, are not admitted because of historical preservation regulations. Despite the coastal position, the use of surface or ground water was not achievable in this case. In fact, the withdrawal from wells is absolutely forbidden in Venice, due to the risk of subsidence of the soil. In addition, as often happens in Venice, the internal channels next to the building have insufficient water flow rate. The experimental analysis highlights very satisfactory performance especially in comparison with the alternative use of air source heat pumps. The high humidity of the soil and the underground water flow present even in the surface layers of the soil promote the quick thermal rebalancing in the borehole field. For the same reason, although there is unbalance between the heat rejected in summer and the one extracted during winter, no consequent thermal degradation of the ground heat exchange is encountered.
Luigi Schibuola; Massimiliano Scarpa. Ground source heat pumps in high humidity soils: An experimental analysis. Applied Thermal Engineering 2016, 99, 80 -91.
AMA StyleLuigi Schibuola, Massimiliano Scarpa. Ground source heat pumps in high humidity soils: An experimental analysis. Applied Thermal Engineering. 2016; 99 ():80-91.
Chicago/Turabian StyleLuigi Schibuola; Massimiliano Scarpa. 2016. "Ground source heat pumps in high humidity soils: An experimental analysis." Applied Thermal Engineering 99, no. : 80-91.
The increased generation of electricity from renewable energy sources may imply issues in the management of national electric systems, due to the low predictability of electricity generation from renewable energy sources. In this regard, interesting opportunities come from the use of advanced control strategies for the management of electric devices such as heat pumps, able to exploit dynamic electricity prices aimed to induce users to absorb/generate electricity according with the electricity grid imbalance. In this paper, the results of building energy simulations applying tailored heat pump control strategies suitable for the integration within smart grids are presented. In particular, three heat pump control strategies are applied, whose action is based on the cost of electricity (absolute and relative to the following 12 h) and on the level of the local electricity generation from photovoltaics. The analyzed control strategies ensure relevant money savings (up to 30%) and increase the degree of energy self-consumption (up to 12% for exported electricity and 22% for imported electricity). The analysis considers also comfort issues and shows that the best results may be achieved commanding the heat pump basing on the generation of the photovoltaic system as well as on the simultaneous heating/cooling loads and electricity prices
Luigi Schibuola; Massimiliano Scarpa; Chiara Tambani. Demand response management by means of heat pumps controlled via real time pricing. Energy and Buildings 2015, 90, 15 -28.
AMA StyleLuigi Schibuola, Massimiliano Scarpa, Chiara Tambani. Demand response management by means of heat pumps controlled via real time pricing. Energy and Buildings. 2015; 90 ():15-28.
Chicago/Turabian StyleLuigi Schibuola; Massimiliano Scarpa; Chiara Tambani. 2015. "Demand response management by means of heat pumps controlled via real time pricing." Energy and Buildings 90, no. : 15-28.
Luigi Schibuola; Massimiliano Scarpa; Chiara Tambani. Modelling of Chillers for Energy Performance Assessment of HVAC Systems. Journal of Energy and Power Engineering 2014, 8, 1 .
AMA StyleLuigi Schibuola, Massimiliano Scarpa, Chiara Tambani. Modelling of Chillers for Energy Performance Assessment of HVAC Systems. Journal of Energy and Power Engineering. 2014; 8 (6):1.
Chicago/Turabian StyleLuigi Schibuola; Massimiliano Scarpa; Chiara Tambani. 2014. "Modelling of Chillers for Energy Performance Assessment of HVAC Systems." Journal of Energy and Power Engineering 8, no. 6: 1.
Luigi Schibuola; Chiara Tambani; Angelo Zarrella; Massimiliano Scarpa. Ground source heat pump performance in case of high humidity soil and yearly balanced heat transfer. Energy Conversion and Management 2013, 76, 956 -970.
AMA StyleLuigi Schibuola, Chiara Tambani, Angelo Zarrella, Massimiliano Scarpa. Ground source heat pump performance in case of high humidity soil and yearly balanced heat transfer. Energy Conversion and Management. 2013; 76 ():956-970.
Chicago/Turabian StyleLuigi Schibuola; Chiara Tambani; Angelo Zarrella; Massimiliano Scarpa. 2013. "Ground source heat pump performance in case of high humidity soil and yearly balanced heat transfer." Energy Conversion and Management 76, no. : 956-970.