A Simplified Thermal Model to Control the Energy Fluxes and to Improve the Performance of Buildings

Danza, Ludovico; Belussi, Lorenzo; Meroni, Italo; Salamone, Francesco; Floreani, Fabio; Piccinini, A.; Dabusti, Alberto

doi:10.1016/j.egypro.2016.11.013

Cited by 35 publications

(16 citation statements)

References 18 publications

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“…The current framework can face this issue by exploiting the flexibility and the reliability of the low cost devices integrated in a Wireless Sensor Network. Finally, the interaction between the environmental parameters, such as CO 2 concentration and illuminance level, and the users’ thermal sensation will be investigated by exploiting the potential provided by ML techniques, thus allowing to include other aspects related to IEQ: Indoor Air Quality (IAQ), Indoor Lighting Quality (ILQ) [ 66 , 67 , 68 ], Acoustic comfort [ 69 , 70 , 71 ] and their interaction with the energy performance of buildings [ 72 , 73 , 74 , 75 ].…”

Section: Discussionmentioning

confidence: 99%

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

Salamone

Belussi

Currò³

et al. 2018

Sensors

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Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment.

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Section: Discussionmentioning

confidence: 99%

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

Salamone

Belussi

Currò³

et al. 2018

Sensors

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“…Traditional resistance and capacitor (RC) networks are widely used to model thermodynamics of building envelopes [15]. Both highly detailed models with variables for many zones, e.g., [26][27][28], and low order models with a single lumped zone, e.g., [17,18], have been proposed in the literature. As the envisioned purpose of the framework is to integrate large clusters of buildings into the grid, a low order thermal building model is most appropriate, considering the resulting computational complexity.…”

Section: Building Thermal Comfort Modelmentioning

confidence: 99%

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

2020

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We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements.

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“…A wide number of tools and methods, with different degrees in detail, has been developed to reach this goal. Among them, simplified but accurate methodologies represent an active area of investigation [2][3]. Energy Performance Certificates (EPCs) are useful tools introduced by EU directives in order to express the level of energy efficiency of buildings and increase awareness among both tenants and buyers allowing for comparison of buildings and building units in terms of energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Simplified tool for the energy performance assessment of residential buildings

Belussi¹,

Danza²,

Meroni³

et al. 2018

MMC_B

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Building sector is responsible for approximately 40% of energy consumption and 36% of CO2 emissions in the EU. For more than a decade the Energy Performance Certification (EPC) revealed to be an effective tool to create demand for energy efficiency in buildings providing recommendations for the cost-effective upgrading of the energy performance. The EPC process is founded on a standard calculation, based on conventional climate, use, surroundings and occupant-related input data, as defined by the Technical Standard EN 15603:2008. Even if the EPC is substantially mandatory in the European Countries, differences can be found along the process in particular in terms of methodology and tools. In Italy the national regulation provides simplified methodologies that can generate results assuring a maximum deviation between + 20% and -5% of the final non-renewable primary energy compared to the same parameters determined with the application of the national reference tool. The aim of the present article is to describe the salient features of the methodology and the technical choices necessary to guarantee the range of acceptability of the results. A case study tested the procedure and the results were compared to those of an extended calculation procedure.

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A Simplified Thermal Model to Control the Energy Fluxes and to Improve the Performance of Buildings

Cited by 35 publications

References 18 publications

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Simplified tool for the energy performance assessment of residential buildings

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