Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. Fanger\u27s Predicted Mean Vote model is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences

Shear characteristics and mesoscopic damage mechanism of long time soaking red sandstone under loading and unloading conditions

The bank slope rock mass below the flood control restricted water level (145 m) in the Three Gorges Reservoir area has experienced long-term immersion in the process of reservoir water lifting and lowering; the water level changes lead to the rock mass being subjected to two working conditions, tangential loading shearing and normal unloading shearing. The difference in rock shearing characteristics directly affects the stability evaluation of bank slopes in different reservoir operation stages. Tangential loading and normal unloading shear tests were carried out on the typical feldspar quartz sandstone after different soaking days; then the characteristics changes of sandstone shear under the two stress conditions were obtained. The microscopic mechanism of the differences was revealed by solution test, SEM test, and nuclear magnetic resonance test. The results show that: (1) compared with the initial sample, after soaking for 80 days, the cohesion loss of the sample is greater than the loss of internal friction angle. The cohesion of the sample under tangential loading is reduced by 40.5%, and the internal friction Angle is only reduced by 2%, while the cohesion of the sample under normal unloading shear is reduced by 31%, and the internal friction Angle is reduced by 8%. (2) The long-term immersion of the sample results in the dissolution of the cement minerals, the gradual development and penetration of the secondary pores, and the increase of porosity. After 60 days of immersion, basically， the water content, porosity, and pore structure of the sample reach a stable state; the particle skeleton overcoming the shear action is almost no longer affected by the soaking water. This is the reason why the shear properties of the samples with long-term water saturation gradually weaken and become stable. (3) Under normal unloading shear conditions, the deviation between the main crack surface and the theoretical shear surface increases; the fracture surface is more inclined to form “S” and “M” types. The increase of the actual shear plane increases the peak shear stress that the rock can bear. Because the biggest contribution to the rock is the skeleton particle, the internal friction angle is larger, and the filling cementing material that provides cohesion makes less contribution to the tensile shear failure. The cohesion obtained by normal unloading is also lower. This study can provide basic information for the stability evaluation of wading slope in reservoir area with the fluctuation of water level and the selection of test method considering the actual working conditions

An improved region growing algorithm in 3D laser point cloud identification of rock mass structural plane

The rock mass structural plane constitutes the weakest part of the rock mass. Accurate and efficient identification of rock mass structural plane and extraction of characteristic information can provide an important basis for the rock mass stability evaluation. 3D laser scanning technology can greatly improve the efficiency and accuracy of structural surface survey; however, the current mainstream point cloud analysis algorithms exist the problems that the edge recognition of structural surfaces is blurred and the accuracy of point cloud segmentation cannot meet the accuracy of structural surface feature information extraction. Considering the spatial relationship between the position of the point cloud of the rock mass structural plane and its neighborhood, the region growth segmentation parameters were corrected by multiple eigenvalues. The KD-tree data structure was used to perform the nearest neighbor search. The voxel was sampled, and the structural plane was segmented to realize the extraction of the structure plane occurrence, spacing, and extension information, based on the normal vector difference of the point cloud and the characteristic final value. The effectiveness of this method in structural plane identification was also verified by indoor models. The results show that compared with the traditional Principal Component Analysis method and Random Sample Consensus method, this method has a higher recognition rate and accuracy in the same area among the 24 structural planes composed of indoor block models. It can not only ensure the complete recognition of data in the complex and changing plane area, but also better segment the edge points in the sharp position of the plane. Using this method, 24 structural planes can be divided into 6 groups, and the corresponding structural plane feature information can be obtained. Compared with the actual measurement results, the angle information error is approximately 1°, and the distance information error is within 1 cm. This method identified three groups of structural planes in the Mangshezhai slope rock mass successfully in the main stream of the Yangtze River. The method proposed in this study has a good verification effect on indoor model and field slope, which can provide robust and effective technical support for the identification and segmentation of rock mass structural plane

An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

The net-zero global carbon target for 2050 needs both expansion of renewable energy and substantive energy consumption reduction. Many of the solutions needed are expensive. Controlling HVAC systems in buildings based upon thermal comfort, not just temperature, uniquely offers a means for deep savings at virtually no cost. In this study, a more accurate means to quantify the savings potential in any building in which smart WiFi thermostats are present is developed. Prior research by Alhamayani et al. leveraging such data for individual residences predicted cooling energy savings in the range from 33 to 47%, but this research was based only upon a singular data-based model of indoor temperature. The present research improves upon this prior research by developing LSTM neural network models for both indoor temperature and humidity. Validation errors are reduced by nearly 22% compared to the prior work. Simulations of thermal comfort control for the residences considered yielded potential savings in the range of 29–43%, dependent upon both solar exposure and insulation characteristics of each residence. This research paves the way for smart Wi-Fi thermostat-enabled thermal comfort control in buildings of all types

Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences

An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

The net-zero global carbon target for 2050 needs both expansion of renewable energy and substantive energy consumption reduction. Many of the solutions needed are expensive. Controlling HVAC systems in buildings based upon thermal comfort, not just temperature, uniquely offers a means for deep savings at virtually no cost. In this study, a more accurate means to quantify the savings potential in any building in which smart WiFi thermostats are present is developed. Prior research by Alhamayani et al. leveraging such data for individual residences predicted cooling energy savings in the range from 33 to 47%, but this research was based only upon a singular data-based model of indoor temperature. The present research improves upon this prior research by developing LSTM neural network models for both indoor temperature and humidity. Validation errors are reduced by nearly 22% compared to the prior work. Simulations of thermal comfort control for the residences considered yielded potential savings in the range of 29–43%, dependent upon both solar exposure and insulation characteristics of each residence. This research paves the way for smart Wi-Fi thermostat-enabled thermal comfort control in buildings of all types

An Improved Method to Estimate Savings from Thermal Comfort Control in Residences from Smart Wi-Fi Thermostat Data

The net-zero global carbon target for 2050 needs both expansion of renewable energy and substantive energy consumption reduction. Many of the solutions needed are expensive. Controlling HVAC systems in buildings based upon thermal comfort, not just temperature, uniquely offers a means for deep savings at virtually no cost. In this study, a more accurate means to quantify the savings potential in any building in which smart WiFi thermostats are present is developed. Prior research by Alhamayani et al. leveraging such data for individual residences predicted cooling energy savings in the range from 33 to 47%, but this research was based only upon a singular data-based model of indoor temperature. The present research improves upon this prior research by developing LSTM neural network models for both indoor temperature and humidity. Validation errors are reduced by nearly 22% compared to the prior work. Simulations of thermal comfort control for the residences considered yielded potential savings in the range of 29–43%, dependent upon both solar exposure and insulation characteristics of each residence. This research paves the way for smart Wi-Fi thermostat-enabled thermal comfort control in buildings of all types