Optimization model for evaluating on-site renewable technologies with storage in zero/nearly zero energy buildings

González-Mahecha, Rosa Esperanza; Lucena, André F.P.; Szklo, Alexandre; Ferreira, Paula Varandas; Vaz, A. Ismael F.

doi:10.1016/j.enbuild.2018.04.027

Cited by 29 publications

(7 citation statements)

References 20 publications

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“…Ghose et al [159] implemented a regression analysis using Kruskal-Wallis (KW) and GAM to support interpretation of = lifecycle assessment (LCA) data for the refurbishment of office buildings in New-Zealand under four different scenarios: business-as-usual, integration of PV panels, integration with an electric renewable-energy grid, and implementation of best construction practices. González-Mahecha et al [160] proposed a model to evaluate the impact of renewable-energy technologies for zero-or nearly-zero-energy buildings (ZEB and NZEB). GAMS was used to calculate, for every system and on hourly basis, the demand and production of power, using as a case study a real building in Portugal.…”

Section: Generalized Additive Modelsmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

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Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

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Section: Generalized Additive Modelsmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

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“…Concerning the optimal design of energy systems with local regulations considerations, the FiT is among the most studied mechanisms for a variety of applications, including hospitals [45][46][47], municipality buildings [48], district heating and cooling networks [49], cities [50], and even regions [51]. Other legal conditions have been identified, such as fuel tax exemption [38], self-consumption obligation and minimum equivalent electrical efficiency [52], carbon tax [50], and a premium scheme [51].…”

Section: Legal Constraints In the Synthesis Of Energy Systems For Buildingsmentioning

confidence: 99%

Assessing the influence of legal constraints on the integration of renewable energy technologies in polygeneration systems for buildings

Pina

Lozano

Serra

2021

Renewable and Sustainable Energy Reviews

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“…Other papers have developed their research applying EnergyPlus ; for instance Loukaidou et al 17 carried out experiments with several test-cell buildings for cost-optimal analysis according to the characteristics of the building envelope; Kang 18 performed some research to optimize the economic sustainability of the “Life Cycle Cost” with the aim of applying energy saving methods from the early phases in the design of a building; Becchio et al 19 presented a roadmap to establish net zero energy balance measures with high energy efficient technical systems to optimize the insulation of the envelope, which was tested on a single family house nZEB in Northern Italy; Barthelmes et al 20 explained an example of a newly built single-family house applying a cost-optimal methodology from a preliminary design phase of the project; Adhikari et al 21 proved that a zero energy building can have a viable investment cost, especially if Photovoltaic (PV) facilities are used. Furthermore, the CPLEX tool was also used in the case of González et al 22 , who assessed an optimization model to evaluate on-site RES composed by mini-wind turbines, solar PVs and a battery storage which was tested in a real building in Portugal to minimize its annual energy costs.…”

Section: Introductionmentioning

confidence: 99%

Energy and thermal modelling of an office building to develop an artificial neural networks model

Santos-Herrero

López-Guede

Flores-Abascal

et al. 2022

Sci Rep

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Nowadays everyone should be aware of the importance of reducing CO2 emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before undertaking a modification in any part of a building focused on improving the energy performance, it is generally better to carry out simulations to evaluate its effectiveness. Using Artificial Neural Networks (ANNs) allows a digital twin of the building to be obtained for specific characteristics without using very expensive software. This can simulate the effect of a single or combined intervention on a particular floor or an event on the remaining floors. In this paper, an example has been developed based on ANN. The results show a reasonable correlation between the real data of the Operative Temperature with the Energy Consumption and their estimates obtained through an ANN model, trained using an hourly basis, on each of the floors of an office building. This model confirms it is possible to obtain simulations in existing public buildings with an acceptable degree of precision and without laborious modelling, which would make it easier to achieve the nZEB target, especially in existing public office buildings.

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Optimization model for evaluating on-site renewable technologies with storage in zero/nearly zero energy buildings

Cited by 29 publications

References 20 publications

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Assessing the influence of legal constraints on the integration of renewable energy technologies in polygeneration systems for buildings

Energy and thermal modelling of an office building to develop an artificial neural networks model

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