A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency

Carli, Raffaele; Dotoli, Mariagrazia; Pellegrino, Roberta; Ranieri, Luigi

doi:10.1109/tsmc.2016.2521836

Cited by 53 publications

(40 citation statements)

References 50 publications

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“…Substituting Equation (8) and Equation (12) in Equation (4) it is possible to obtain PMV as a non-linear function of ψ, T, and T cl . We then linearize the clothing temperature function of Equation (5) considering that in comfort situation the air temperature T varies in the interval 20 ÷ 22°C. In particular, we consider a linear approximation of the clothing temperature for the air temperature variation interval 17 ÷ 25°C.…”

Section: Thermal Comfort Assessment By Pmvmentioning

confidence: 99%

“…In recent times, the increase of energy efficiency is a pivotal goal for energy policy makers that aim at promoting a conscious, cost-effective, and sustainable energy use in management of resources and infrastructures [1], transportation and logistics [2], the production and industrial sector [3], and all activities related to human life [4]. A significant part of energy consumption concerns the energy demand of buildings that in Europe amounts to 40% of the total and is mainly due to the so-called Heating, Ventilation and Air Conditioning (HVAC) systems [5]. HVAC systems are devoted to guarantee hygrothermal comfort in building indoor environments [6] and their automated management can largely impact the virtuous behavior of its end-users.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

Carli

Cavone

Othman

et al. 2020

Sensors

Self Cite

109

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The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users' interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.

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Section: Thermal Comfort Assessment By Pmvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

Carli

Cavone

Othman

et al. 2020

Sensors

Self Cite

109

View full text Add to dashboard Cite

show abstract

“…In this scenario, the research community is studying novel energy efficient production strategies [2], [3], efficient buildings [4], smart grid technologies [5], and techniques for predicting electric loads [6] to determine the optimal price of renewable energy [7]. Among these topics, increasing renewable energy production and improving distribution are of paramount importance to the global economy.…”

Section: Introductionmentioning

confidence: 99%

“…However, the market for renewable energy penalizes producers when their estimated power production differs from that actually delivered [11]. To avoid penalties and maximize their income, companies need accurate power predictions and decision support systems [4], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Wind Power Production

Labati

Genovese

Piuri

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for traders operating in the energy market and for managers in charge of planning the non-renewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze wind farm production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on weather forecasts; thus, it is suitable for wind farms that cannot collect or manage real-time data acquired by sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of wind farm orography. We tuned and evaluated the proposed system using data collected from two wind farms over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and impact of our system at ten different wind farms under real-world conditions and achieved a significant improvement with respect to their previous approach.

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“…In general, to improve buildings' performance from an energy efficiency point of view, it is necessary to take into account three different factors: main structural elements of the building, their use and the users of the building. The optimization and control methodologies for buildings can be classified into two approaches: (i) based on a real-time operation of the building, i.e., using a building energy management system; and (ii) an offline approach based on strategic choices for structural changes [6][7][8]. Additionally, it should be taken into account that users' comfort and energy efficiency are conflicting objectives since, in order to reach an optimal comfort situation, it is often required to increase energy consumption.…”

Section: Introductionmentioning

confidence: 99%

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

et al. 2017

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Abstract:The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.

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A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency

Cited by 53 publications

References 50 publications

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

A Decision Support System for Wind Power Production

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

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