180,969 research outputs found
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Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States
A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking
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Building thermal load prediction through shallow machine learning and deep learning
Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty
DISCHARGE OXIDE STORAGE CAPACITY AND VOLTAGE LOSS IN LI-AIR BATTERY
Air cathodes, where oxygen reacts with Li ions and electrons with discharge oxide stored in their pore structure, are often considered as the most challenging component in nonaqueous Lithium-air batteries. In non-aqueous electrolytes, discharge oxides are usually insoluble and hence precipitate at local reaction site, raising the oxygen transport resistance in the pore network. Due to their low electric conductivity, their presence causes electrode passivation. This study aims to investigate the air cathode's performance through analytically obtaining oxygen profiles, modeling electrode passivation, evaluating the transport polarization raised by discharge oxide precipitate, and developing analytical formulas for insoluble Li oxides storage capacity. The variations of cathode quantities, including oxygen content and temperature, are evaluated and related to a single dimensionless parameter - the Damköhler Number (Da). An approximate model is developed to predict discharge voltage loss, along with validation against two sets of experimental data. Air cathode properties, including tortuosity, surface coverage factor and the Da number, and their effects on the cathode's capacity of storing Li oxides are formulated and discussed
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Fabrication of a high sensitive Ag-nanoparticle substrate and its application to the detection of toxic substances
Surface Enhanced Raman Scattering (SERS) is typically observed with the substrate in a liquid medium and it has been proposed as a promising technique for detecting low levels of pollutants in liquids. A technique is presented for self-assembly to immobilize Ag nanoparticles (Ag-NPs), with diameters ranging from 100 to 800nm on a solid support. Experimental results have been obtained through experiments using Ag-NPs active substrates to detect Rhodamine 6G (R6G) and crystal violet in the deionized water. Further, the SERS spectrum and Raman spectrum of phoxim were also measured, showing the enhancement in the performance of the active substrate as a result
Magnetization reversal through synchronization with a microwave
Based on the Landau-Lifshitz-Gilbert equation, it can be shown that a
circularly-polarized microwave can reverse the magnetization of a Stoner
particle through synchronization. In comparison with magnetization reversal
induced by a static magnetic field, it can be shown that when a proper
microwave frequency is used the minimal switching field is much smaller than
that of precessional magnetization reversal. A microwave needs only to overcome
the energy dissipation of a Stoner particle in order to reverse magnetization
unlike the conventional method with a static magnetic field where the switching
field must be of the order of magnetic anisotropy.Comment: 4 pages, 5 figure
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
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