Bibliografie tematiche / Thermostat programming

Letteratura scientifica selezionata sul tema "Thermostat programming"

Autore: Grafiati

Pubblicato: 25 luglio 2024

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Articoli di riviste sul tema "Thermostat programming"

Rodríguez de Rivera, Pedro Jesús, Miriam Rodríguez de Rivera, Fabiola Socorro, Manuel Rodríguez de Rivera e Gustavo Marrero Callicó. "A Method to Determine Human Skin Heat Capacity Using a Non-Invasive Calorimetric Sensor". Sensors 20, n. 12 (17 giugno 2020): 3431. http://dx.doi.org/10.3390/s20123431.

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A calorimetric sensor has been designed to measure the heat flow dissipated by a 2 × 2 cm2 skin surface. In this work, a non-invasive method is proposed to determine the heat capacity and thermal conductance of the area of skin where the measurement is made. The method consists of programming a linear variation of the temperature of the sensor thermostat during its application to the skin. The sensor is modelled as a two-inputs and two-outputs system. The inputs are (1) the power dissipated by the skin and transmitted by conduction to the sensor, and (2) the power dissipated in the sensor thermostat to maintain the programmed temperature. The outputs are (1) the calorimetric signal and (2) the thermostat temperature. The proposed method consists of a sensor modelling that allows the heat capacity of the element where dissipation takes place (the skin) to be identified, and the transfer functions (TF) that link the inputs and outputs are constructed from its value. These TFs allow the determination of the heat flow dissipated by the surface of the human body as a function of the temperature of the sensor thermostat. Furthermore, as this variation in heat flow is linear, we define and determine an equivalent thermal resistance of the skin in the measured area. The method is validated with a simulation and with experimental measurements on the surface of the human body.

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Stopps, Helen, e Marianne F. Touchie. "Residential smart thermostat use: An exploration of thermostat programming, environmental attitudes, and the influence of smart controls on energy savings". Energy and Buildings 238 (maggio 2021): 110834. http://dx.doi.org/10.1016/j.enbuild.2021.110834.

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Gao, J.-P., G.-M. G. Zhu, E. G. Strangas e F.-C. Sun. "Equivalent fuel consumption optimal control of a series hybrid electric vehicle". Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 223, n. 8 (1 agosto 2009): 1003–18. http://dx.doi.org/10.1243/09544070jauto1074.

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Improvements in hybrid electric vehicle fuel economy with reduced emissions strongly depend on their supervisory control strategy. In order to develop an efficient real-time supervisory control strategy for a series hybrid electric bus, the proposed equivalent fuel consumption optimal control strategy is compared with two popular strategies, thermostat and power follower, using backward simulations in ADVISOR. For given driving cycles, global optimal solutions were also obtained using dynamic programming to provide an optimization target for comparison purposes. Comparison simulations showed that the thermostat control strategy optimizes the operation of the internal combustion engine and the power follower control strategy minimizes the battery charging and discharging operations which, hence, reduces battery power loss and extends the battery life. The equivalent fuel consumption optimal control strategy proposed in this paper provides an overall system optimization between the internal combustion engine and battery efficiencies, leading to the best fuel economy.

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Chen, Chun-Chi, e Shih-Hao Lin. "A Time-Domain CMOS Oscillator-Based Thermostat with Digital Set-Point Programming". Sensors 13, n. 2 (29 gennaio 2013): 1679–91. http://dx.doi.org/10.3390/s130201679.

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Vavrinevych, O., D. Hyrenko, T. Hyrenko, S. Omelchuk e A. Syrota. "Improvement of analytical determination of residual quantities of active substances of the triasole class in juices". Zurnal Hromatograficnogo tovaristva 20, n. 66 (24 novembre 2020): 24–32. http://dx.doi.org/10.15407/zht2020.66.024.

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Today, the most common among fungicides approved for use in Ukraine, as well as in other countries, are compounds of the triazole class. Juices, as a product of processing agricultural raw materials, can be consumed by a wide range of the population. The purpose of work was to develop analytical methods for compound of triazole (difenoconazole and propiconazole) in juices to ensure quality control of agricultural products grown with the use of these fungicides. Conditions for chromatography of difenoconazole and propiconazole using gas-liquid chromatography with capillary column SH-Rxi-5ms (30 m 0,25 mm) and electron capture detector were determined. Optimal conditions of difenoconazole chromatography were established: thermostat column temperature - 280 C, evaporator temperature - 290 C, detector thermostat temperature - 300 C. Retention time: isomer 1 - (6,91 +- 0,1) minutes, isomer 2 - (7,04 +- 0,1) minutes. The linear range of detection is 0,01-0,04 ug/cm3. The optimal conditions for chromatography of propiconazole were also established: the temperature of the column thermostat in the programming mode: initial T - 100 C with an exposure of 1 minute; from 90 C to 260 C - rate of temperature rise 40 C/min; exposure - 7,45 minutes; evaporator temperature - 270 C, detector temperature - 280 C. The retention time under these conditions was 3,49 +- 0,1 minutes for isomer 1 and 3,57 +- 0,1 minutes for isomer 2. The linear range of detection is 0,01-0,05 ug/cm3. The calibration dependence of the area of peaks of the investigated substances on their concentration was established and described by the linear regression equation.

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Sahu, Kirti Sundar, Joel A. Dubin, Shannon E. Majowicz, Sam Liu e Plinio P. Morita. "Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights". JMIR Public Health and Surveillance 10 (20 marzo 2024): e46903. http://dx.doi.org/10.2196/46903.

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Background The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google’s GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. Objective This study investigates in-home mobility data from ecobee’s smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google’s residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. Methods Motion sensor data were acquired from the ecobee “Donate Your Data” initiative via Google’s BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces—Ontario, Quebec, Alberta, and British Columbia—during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. Results The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google’s data set. Examination of Google’s daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. Conclusions This study’s findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google’s out-of-house residential mobility data and ecobee’s in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

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Davoodi, Jamal, e Samaneh Khoshkhatti. "Molecular Dynamics Simulation of Thermal Conductivity of Aluminum". Defect and Diffusion Forum 336 (marzo 2013): 47–55. http://dx.doi.org/10.4028/www.scientific.net/ddf.336.47.

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In this research, the thermal conductivity of aluminum (Al) in macro scale was investigated by the molecular dynamics simulation technique. We used FORTRAN programming in the simulations and used a fixed number of atoms, N, confined to a fixed pressure, P, and maintained at a constant preset temperature, T, i.e. the NPT ensemble. The Sutton-Chen many-body potential was used to calculate energy and force. The temperature and pressure of the system were controlled by Nosé-Hoover thermostat and Berendsen barostat respectively. We could solve the equations of motion using the Velocity Verlet algorithm. We calculated the thermal conductivity of Al in the macro scale using the Green-Kubo method. Moreover, we have studied the effect of increasing temperature on the value of the thermal conductivity of Al. The obtained results showed that the computed thermal conductivities are in good agreement with experimental data.

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Kamyar, Reza, e Matthew M. Peet. "Optimal Thermostat Programming for Time-of-Use and Demand Charges With Thermal Energy Storage and Optimal Pricing for Regulated Utilities". IEEE Transactions on Power Systems 32, n. 4 (luglio 2017): 2714–23. http://dx.doi.org/10.1109/tpwrs.2016.2618374.

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Riski, Sestri Novia. "SISTEM PAKAR UNTUK MENDETEKSI KESALAHAN ELEKTRODA PADA PROSES WELDING FRAME THERMOSTAT PADA SOULPLATE MENGGUNAKAN METODE FORWARD CHAINING BERBASIS WEB (STUDI KASUS PT PHILIPS)". Edik Informatika 3, n. 2 (9 ottobre 2017): 211–25. http://dx.doi.org/10.22202/ei.2017.v3i2.2248.

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The use of SMAW (Shielded Metal Arc Welding) in the industrial world is enough widely used. With this machine the human greatly helped by the need to make a metal object. So that by the frequent use of these tools, the more susceptible it is also a tool damage. Technicians machines supplied by the company are not proportional to the number of machines. Therefore, to help resolve this issue needs an expert system that can act as an assistant to the engineer. Data relating to the damage processed using forward chaining expert system. Expert systems are made using the programming language PHP and MySQL database so as to produce an expert system to detect damage SMAW web based. So it can help technicians solve problems SMAW and can also be used for the source of knowledge about the condition and its causes and solutions to overcome them.

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Bondar, Oleksiy. "Predictive Neural Network in Multipurpose Self-Tuning Controller". Acta Mechanica et Automatica 14, n. 2 (1 giugno 2020): 114–20. http://dx.doi.org/10.2478/ama-2020-0017.

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AbstractA very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.

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Libri sul tema "Thermostat programming"

Programming the digital thermostat 947. Author and engineer, 2023.

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Capitoli di libri sul tema "Thermostat programming"

Antunes, Carlos Henggeler, Vahid Rasouli, Maria João Alves, Álvaro Gomes, José J. Costa e Adélio Gaspar. "A Discussion of Mixed Integer Linear Programming Models of Thermostatic Loads in Demand Response". In Trends in Mathematics, 105–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32157-4_7.

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Singh, Arun Kumar. "Applications of the Internet of Things and Machine Learning Using Python in Digital Marketing". In Advances in Marketing, Customer Relationship Management, and E-Services, 213–32. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8166-0.ch011.

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The internet of things (IoT) is a network of physical objects or “things” embedded with electronics, software, sensors, and network connectivity, which enables them to collect and exchange data. These objects can include everyday devices such as smart thermostats, home security systems, wearable fitness trackers, and even vehicles. However, IoT also raises concerns about privacy, security, and reliability, as the increasing number of connected devices creates new opportunities for cyber attacks and data breaches. As such, the development and deployment of IoT technology must be done in a responsible and secure manner. Through the use of sensors, it is now possible to collect, transmit, and transform data into knowledge. The marketing and sales sectors are capable of using data gathered through IoT to interact with customers on an individual basis. Python is an object oriented, scripted and interpreted language for both learning and real-world programming. Work in this chapter first introduces Python as a language, and gives summary about data science, machine learning, and IOT.

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Atti di convegni sul tema "Thermostat programming"

Agarwal, Neha, e Thomas F. Wenisch. "Thermostat". In ASPLOS '17: Architectural Support for Programming Languages and Operating Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3037697.3037706.

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Gao, Ge, e Kamin Whitehouse. "The self-programming thermostat". In the First ACM Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1810279.1810294.

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Kamyar, Reza, e Matthew M. Peet. "Optimal thermostat programming and optimal electricity rates for customers with demand charges". In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7172042.

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Mikluscak, Tomas, Andrej Kapjor, Ales Janota e Ondrej Biros. "Exploring possibilities of predictive self-programming thermostats for energy savings". In 2012 ELEKTRO. IEEE, 2012. http://dx.doi.org/10.1109/elektro.2012.6225615.

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Chan, Korey, e Saeid Bashash. "Modeling and Energy Cost Optimization of Air Conditioning Loads in Smart Grid Environments". In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5284.

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Electricity for heating, ventilation, and air condition (HVAC) machines takes up a large percentage of energy consumption in the buildings and thus in turn, a large portion of the energy monetary cost. Optimization of air conditioners use throughout the day will reduce energy consumption and expenditure. This study introduces a second-order differential equation model to capture the indoor temperature dynamics of a building. An experimental test bed is developed to collect a set of indoor/outdoor temperature and sunlight data. Using a least-squares-based system identification process, the model parameters are identified and checked through simulation. Optimization of the room temperature is then determined by solving a mixed-integer quadratic programming problem in relation to the hourly-updated energy prices. Mixed-integer quadratic programming solution is compared to a two-point thermostatic control system. A hybrid solution compromising the quadratic programming algorithm and the conventional thermostatic control scheme is proposed as a tractable approach for the near-optimal energy management of the system.

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Solouk, Ali, Mahdi Shahbakhti e Mohammad J. Mahjoob. "Energy Management and Control of a Hybrid Electric Vehicle With an Integrated Low Temperature Combustion (LTC) Engine". In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6286.

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Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.

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Wang, Chenli, e Hohyun Lee. "Economical and Non-Invasive Residential Human Presence Sensing via Temperature Measurement". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88211.

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Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption in United States, encompassing about 25% of total residential energy usage. A significant portion of energy is wasted by unnecessary operation, such as overheating/overcooling or operation without occupants. Wasteful behaviors will consume twice the amount of energy compared to energy conscious behaviors. Many market programmable thermostats exist to address this problem, however, difficulties in persistent programming of such products and lack of understanding of underlying physics prevent users from achieving tangible impact. Hence, fully autonomous energy control system is desirable to engage as many people into energy conscious behaviors as possible. Occupancy measurement is necessary components to enable fully autonomous control. Occupancy information can save energy by automatically turn off the HVAC system when the building is not occupied, or floats to a more energy-efficient setback temperature when the activity level is low. A number of existing sensor solutions available on the market include Passive Infrared (PIR), ultrasonic, Bluetooth/GPS, and CO2 sensors, but these are either too expensive, not user-friendly, or limited in detection scope. These sensors are also incapable of detecting whether or not the occupant is an animal or a human. The work in this paper proposes an economical, reliable, non-invasive package to both detect human presence in a residence of a wide variety of geometries at the time and predict future occupancy pattern, by utilizing temperature sensors. To accomplish this, thermal sensors will be attached to both ends of door handles to collect the temperature data. This data will allow us to create a schedule to identify human activity leaving and exiting the space. At the same time, we will be collecting the skin temperature to determine the human activity level for better identification of the thermal comfort zone for occupants. The prediction model for occupancy pattern will be developed from previous data by using machine learning algorithm. For verification, experimental setup was built to verify our model by comparing actual human presence data from a house with the measured and predicted occupancy pattern from the temperature sensors. Future steps include implementing a data fusion scheme into the model to combine information from multiple types of sensors.

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