1,720,997 research outputs found
A tool for the optimal sensor placement to optimize temperature monitoring in large sports spaces
No full textThe 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
Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV
No full textA 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
Temperature sensing optimization for home thermostat retrofit
No full textMost existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap and relatively easy-to-install retrofit solutions. These systems provide increased freedom of installation, due to the use of wireless communication; however, the uncertainty of the measured air temperature, considering the thermostat placement, could impact the final heating performance. This paper presents a sensing optimization approach for a home thermostat, in order to determine the optimal retrofit configuration to reduce the sensing uncertainty, thus achieving the required comfort level and minimizing the retrofit’s payback period. The methodology was applied to a real case study—a dwelling located in Italy. The measured data and a simulation model were used to create different retrofit scenarios. Among these, the optimal scenario was achieved through thermostat repositioning and a setpoint of 21◦C, without the use of TRVs. Such optimization provided an improvement of control performance due to sensor location, with consequent energy savings of 7% (compared to the baseline). The resulting payback period ranged from two and a half years to less than a year, depending on impact of the embedded smart thermostat algorithms
Optimizing Merkle Proof Size Through Path Length Analysis: A Probabilistic Framework for Efficient Blockchain State Verification
No full textThis study addresses a critical challenge in modern blockchain systems: the excessive size of Merkle proofs in state verification, which significantly impacts scalability and efficiency. As highlighted by Ethereum’s founder, Vitalik Buterin, current Merkle Patricia Tries (MPTs) are highly inefficient for stateless clients, with worst-case proofs reaching approximately 300 MB. We present a comprehensive probabilistic analysis of path length distributions in MPTs to optimize proof size while maintaining security guarantees. Our novel mathematical model characterizes the distribution of path lengths in tries containing random blockchain addresses and validates it through extensive computational experiments. The findings reveal logarithmic scaling of average path lengths with respect to the number of addresses, with unprecedented precision in predicting structural properties across scales from 100 to 300 million addresses. The research demonstrates remarkable accuracy, with discrepancies between theoretical and experimental results not exceeding 0.01 across all tested scales. By identifying and verifying the right-skewed nature of path length distributions, we provide critical insights for optimizing Merkle proof generation and size reduction. Our practical implementation guidelines demonstrate potential proof size reductions of up to 70% through optimized path structuring and node layout. This work bridges the gap between theoretical computer science and practical blockchain engineering, offering immediate applications for blockchain client optimization and efficient state-proof generation
Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms
No full textThis paper presents an approach to assess the measurement uncertainty of human thermal comfort by using an innovative method that comprises a heterogeneous set of data, made by physiological and environmental quantities, and artificial intelligence algorithms, using Monte Carlo method (MCM). The dataset is made up of heart rate variability (HRV) features, air temperature, air velocity and relative humidity. Firstly, MCM is applied to compute the measurement uncertainty of the HRV features: results have shown that among 13 participants, there are uncertainty values in the measurement of HRV features that ranges from ±0.01% to ±0.7 %, suggesting that the uncertainty can be generalized among different subjects. Secondly, MCM is applied by perturbing the input parameters of random forest (RF) and convolutional neural network (CNN) algorithm, trained to measure human thermal comfort. Results show that environmental quantities produce different uncertainty on the thermal comfort: RF has the highest uncertainty due to the air temperature (14 %), while CNN has the highest uncertainty when relative humidity is perturbed (10.5 %). A sensitivity analysis also shows that air velocity is the parameter that causes a higher deviation of thermal comfort
An IoT measurement solution for continuous indoor environmental quality monitoring for buildings renovation
No full textThe measurement of Indoor Environmental Quality (IEQ) requires the acquisition of multiple quantities regarding thermal comfort and indoor air quality. The IEQ monitoring is essential to investigate the building's performance, especially when renovation is needed to improve energy efficiency and occupants' well-being. Thus, IEQ data should be acquired for long periods inside occupied buildings, but traditional measurement solutions could not be adequate. This paper presents the development and application of a non-intrusive and scalable IoT sensing solution for continuous IEQ measurement in occupied buildings during the renovation process. The solution is composed of an IR scanner for mean radiant temperature measurement and a desk node with environmental sensors (air temperature, relative humidity, CO2, PMs). The integration with a BIM-based renovation approach was developed to automatically retrieve building's data required for sensor configuration and KPIs calculation. The system was installed in a nursery located in Poland to support the renovation process. IEQ performance measured before the intervention revealed issues related to radiant temperature and air quality. Using measured data, interventions were realized to improve the envelope insulation and the occupant's behaviour. Results from post-renovation measurements showed the IEQ improvement achieved, demonstrating the impact of the sensing solution
Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV
No full textA 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Application of an IoT infrared sensor for thermal transmittance measurement in building renovation
No full textContinuous measurement of building's thermal performance is relevant to guarantee a comfortable and healthy living environment with minimum energy consumption. The Comfort Eye, an Internet of Things (IoT) infrared sensor, allows to acquire continuously thermographic images and to perform a long-term monitoring in buildings. A mathematical model is applied to the thermographic images to identify the main sources of inefficiencies in building's envelope and to provide real information about the thermal behaviour of the facades. The innovative methodology, the Comfort Eye, can be used for relatively fast and inexpensive U-value estimation of façade walls without the use of additional measurement equipment (e.g., heat flux sensors). The Comfort Eye, scanning the entire walls facing the exterior, allows to identify potential thermal bridges and therefore it can be considered an effective tool to support buildings renovation for performance improvement. This paper presents the application of the methodology in real demonstration cases. The analysis allows to scan all the walls, measure the U-value, with discrepancies between the U-values estimated using Comfort Eye, and U-values estimated using the HFM of 0%-10%, in any point of an entire wall and to delimit thermal bridge as well as its area of greater influence
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