Renovation of a social house into a NZEB: Use of renewable energy sources and economic implications

Luca, Giovanna De; Ballarini, Ilaria; Lorenzati, Alice; Corrado, Vincenzo

doi:10.1016/j.renene.2020.05.170

Cited by 37 publications

(16 citation statements)

References 21 publications

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“…L(θ) takes the value of its lower bound ELBO only when KL(q P) = 0. Thus, the task becomes to find the estimate of θ such that (t) that is one of the specific options for q(Z) in Equation (7). The superscript (t) indicates the values obtained from the last iteration of the EM algorithm.…”

Section: The Expectation-maximization (Em) Algorithmmentioning

confidence: 99%

“…Studies about nearly zero-energy building (NZEB) and positive energy districts (PEDs) have recently drawn much attention to possible ways to reduce this energy demand [3,4]. NZEB buildings have a very high energy performance level, with the nearly zero or very low amount of energy provided by significant renewable sources, and their energy performance is determined on the basis of calculated or actual annual energy usage [5][6][7]. PEDs are defined as energy-efficient and energy-flexible urban areas with a surplus renewable energy production and net zero greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

2021

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In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

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Section: The Expectation-maximization (Em) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

2021

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“…In parallel with the increase in RES penetration, it is expected that certain important energy-intensive sectors, such as mobility [10] and space heating [11], will move towards more sustainable systems powered by electricity. While these new electrical loads may aggravate the grid imbalances, at the same time they will offer new sources of power flexibility that can contribute to balancing the network.…”

Section: Introductionmentioning

confidence: 99%

Demand Flexibility Enabled by Virtual Energy Storage to Improve Renewable Energy Penetration

et al. 2020

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The increasing resort to renewable energy distributed generation, which is needed to mitigate anthropogenic CO2 emissions, leads to challenges concerning the proper operation of electric distribution systems. As a result of the intrinsic nature of Renewable Energy Sources (RESs), this generation shows a high volatility and a low predictability that make the balancing of energy production and consumption difficult. At the same time, the electrification of new energy-intensive sectors (such as heating) is expected. This complex scenario paves the way for new sources of flexibility that will have more and more relevance in the coming years. This paper analyses how the electrification of the heating system, combined with an electric flexibility utilisation module, can be used to mitigate the problems related to the fluctuating production of RES. By using Power-to-Heat (P2H) technologies, buildings are able to store the overproduction of RES in the form of thermal energy for end-use according to the principle of the so-called Virtual Energy Storage (VES). A context-aware demand flexibility extraction based on the VES model and the flexibility upscale and utilisation on district-level through grid simulation and energy flow optimisation is presented in the paper. The involved modules have been developed within the PLANET (PLAnning and operational tools for optimising energy flows and synergies between energy NETworks) H2020 European project and interact under a unified co-simulation framework with the PLANET Decision Support System (DSS) for the analysis of multi-energy scenarios. DSS has been used to simulate a realistic future energy scenario, according to which the imbalance problems triggered by RES overproduction are mitigated with the optimal exploitation of the demand flexibility enabled by VES.

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“…The EU has assumed the leading role in achieving the goals of substitution of fossil fuels with renewable energy sources, reduction of GHG emissions and other environmental impacts [16]. The share of renewable energy sources on the gross final energy consumption has grown up from 11% in 2005 to 19.5% in 2017 [17], although the achievement of these objectives has been quite heterogeneous. In this context, the case of Spain must be highlighted, since the share of electricity production from renewables reached 43.66% in 2020 [18].…”

mentioning

confidence: 99%

“…with a life cycle approach [16,[24][25][26][27][28]. Some of them include an energy study of the use of renewable energy sources [17,29]. Others include both an economic and an environmental analysis to determine the payback period [30,31].…”

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confidence: 99%

Economic and Environmental Assessment on Implementing Solar Renewable Energy Systems in Spanish Residential Homes

2021

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In Europe, buildings are responsible for more than one third of the total final energy demands and greenhouse gas emissions. In the last twenty years, the European Union has published a succession of energy performance of building directives to define and ensure the fulfilment of a series of objectives regarding greenhouse gas emissions, energy consumption, energy efficiency and energy generation from renewable sources in buildings. For its part, Spain is adapting its legal framework, transposing these directives with the aim of achieving greater energy efficiency and sustainability for buildings. Under this context, an energy, economic and environmental assessment is performed to analyze the impact of these regulatory changes on a single-family home including a photovoltaic installation for self-consumption with surpluses and/or a solar thermal installation for domestic hot water supply, located in each one of the eight thousand one hundred thirty-one municipalities that make up Spain. The energy behavior of the original house is compared with that obtained after it is updated with these new facilities. The transient system simulation tool is used for the energy study. The results show that the European objectives are far exceeded. The energy savings achieved range from 67% to 126%, carbon dioxide emissions decrease by 42% to 100% and energy bills are reduced in cost by 32% to 81%. The findings of this work can be used by policymakers as guidelines for the development of national strategic plans and financial incentives for the promotion of small-scale residential photovoltaic and solar thermal applications, as well as by designers, supervisors, managers and developers to include them in their projects.

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Renovation of a social house into a NZEB: Use of renewable energy sources and economic implications

Cited by 37 publications

References 21 publications

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

Demand Flexibility Enabled by Virtual Energy Storage to Improve Renewable Energy Penetration

Economic and Environmental Assessment on Implementing Solar Renewable Energy Systems in Spanish Residential Homes

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