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A fine-grained regulation of the HVAC emitters, capable of providing heat and cool only where effectively needed, can lead to a significant energy saving. This paper presents the results from an experimental test of an energy-efficient sub-zonal heating management system, based on an innovative comfort sensor. The objective is to demonstrate how the real-time PMV (Predicted Mean Vote) measurement of different positions in a room can be used to apply optimal rules for the climate control. The case study is an office, located in Central Italy, equipped with two separately controllable electrical heaters. The heating system has been coupled with a low-cost, IR-based comfort sensor, named Comfort Eye, to regulate the heating output of each heater in function of the local comfort conditions. A PID (Proportional–integral–derivative) controller, tuned by fuzzy logic, uses the PMV measured in the respective sub-zone as controlled variable, regulates the power of each heater. The system ran for one winter day and results have been compared with a reference condition, representative of the typical ON/OFF control of the room. The reference condition has been created with the same heating system, but without the sub-zonal division. The comparison, considering the specific application presented, turned out that the sub-zonal control system could achieve an energy saving up to 17% with respect to the typical ON/OFF control with a slight improvement of thermal comfort, reduced deviation from the neutral condition (PMV = 0). This shows that the possibility of measuring comfort distributions is crucial to achieve optimal environmental control. [ABSTRACT FROM AUTHOR]

The Sensor Optimization Unit (SOU) is a tool, meant to be used by HVAC engineer, for the optimization of temperature sensors placement in large sports spaces, where the HVAC system usually maintains climate conditions actuating control rules based on one temperature sensor, installed in the return air duct or in a single point of the space, without taking into account the indoor air temperature distribution. The SOU characterizes the indoor horizontal air temperature distribution which can be retrieved with a simulation model or field measurement. A dedicated measurement performance index is calculated to determine the optimal sensors location that provides the maximum accuracy with the minimum number of sensors to be deployed. The application and validation of the tool in a real indoor swimming pool outlined that the measurement uncertainty due to the incorrect location of the existing thermostat was higher than ± 0.5 °C (thermostat uncertainty datasheet) for the 42% of the period considered. The optimized placement determined with the SOU decreased that period to 1.5% of the overall time. [ABSTRACT FROM AUTHOR]

A study for the development of a monitoring methodology that includes thermal comfort in a smart metering system for sports facilities is presented. The scope of this system is to obtain meaningful information on how energy is used in relation to the level of comfort perceived, so as to allow fully informed management operations. The main problem to be addressed is the accurate quantitative measurement of the thermal comfort inside indoor sports areas (e.g. swimming pools, fitness rooms, etc.), which in this work is estimated through the PMV (Predicted Mean Vote) index. The paper describes what needs to be dynamically measured, which parameters are required, which critical uncertainty factors may arise and how to estimate the indicators. A sensitivity and uncertainty analysis for the PMV measurement in this specific application is performed to develop a simplified approach. The results obtained from the measurement campaigns made in two different sports areas are presented to validate the approach. Finally, a simulation of swimming pool data is used to preliminarily demonstrate the feasibility and effectiveness of the global monitoring system proposed. [ABSTRACT FROM AUTHOR]

Abstract: Energy procurement is a necessity which needs a deep study of both the demand and the generation sources, referred to consumers territorial localization. The study presented in this paper extends and consolidate the Shimon Awerbuch’s study on portfolio theory applied to the energy planning, in order to define a broad generating mix which optimizes one or more objective functions defined for a determined contest. For this purpose the computation model was specialized in energy generation problem and extended with the addition of new cost-risk settings, like renewable energy availability, and Black–Litterman model, which extends Markowitz theory. Energy planning was then contextualized to the territory: the introduction of geographic and climatic features allows to plan energy infrastructures on both global and local (regional, provincial, municipal) scale. The result is an efficient decision making tool to drive the investment on typical energy policy assets. In general the tool allows to analyze several scenarios in support of renewable energy sources, environmental sustainability, costs and risks reduction. In this paper the model was applied to the energy generation in Italy, and the analysis was done: on the actual energy mix; assuming the use of nuclear technology; assuming the verisimilar improvement of several technologies in the future. [Copyright &y& Elsevier]