Current Based HVAC Systems Air Filter Diagnostics and Monitoring

This paper addresses the use of continuous indoor motor current to detect filter blockage in HVAC system. A commonly known phenomenon exists in the loading of a typical indoor motor blower that results in a power consumption decrease and hence less current draw for PSC motor, and power and currant draw increase for constant torque motor. Testing using Akaike information criterion (AIC), classification and regression tree (CART) models, and using both fixed radius basis function and linear basis function was described and performed against field of installed systems to determine if candidate data filter was sufficient, or to motivate use of Mann-Kendall to determine trend existence, strength, and transition in existence or strength. The bases, commonly used in practice, were found to have cumulative effectiveness against only 50.4% of installed systems, and were strongly differentiated in performance against motor type. The Mann-Kendall approach was found to have performance of ~88% of evaluated systems. This approach calculates the confidence trending level corresponds to the nonparametric correlation coefficient for the indoor current daily averages. Trend levels will be accumulated over time and will be used to declare filter blockage once they suggest a strong trend in the direction of filter blockage.

Comfort-based Optimal Temperature Setpoint Calculation

General practice in current building HVAC control is to select building temperature setpoints that comply with ASHRAE Standard 55. By meeting this standard, based on the PMV comfort model, 80% of building occupants should be satisfied with their thermal environment. However, unfortunately, this is rarely the case. One possible reason for this is the variation in occupant activity and clothing that are usually assumed default values using this standard. In this work, we present an iterative-based algorithm to solve this problem. The algorithm solves the PMV inverse model equation to determine the optimal temperature setpoint while inferring human activity level from the biometric data of wearable fitness devices. The new algorithm is also designed to handle multi-occupants with conflicting comfort preferences scenario. Using this new algorithm, our results show a significant increase in occupant comfort, specifically when occupant activity is high

Utilizing Wearable Devices To Design Personal Thermal Comfort Model

Apart from the common environmental factors such as relative humidity, radiant and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age and health status. This is manifested as a difference in comfort levels between people residing under the same roof, and hence no general comprehensive comfort model satisfying everyone. Current and newly emerging advancements in state of the art wearable technology have made it possible to continuously acquire biometric information. This work proposes to access and exploit this data to build personal thermal comfort model. Relying on various supervised machine learning methods, a personal thermal comfort model will be produced and compared to a general model to show its superior performance

Localized Indoor Temperature Estimation Using Smartphone and Laptop Internal Sensors

This paper investigates a mechanism in which indoor air temperature can be predicted using the temperature sensor on the battery and the CPU of smartphones or laptops, where data is easily accessible and ubiquitous. As a case study, several machine-learning methods were used to build models from a MacBook Pro’s data and the measured surrounding air temperature. The effects of the machine learning type and input feature size (by including other parameters such as CPU processing usage and battery charge percentage) on the model accuracy were investigated. The goal is to determine a set of feature combinations that can be used to build models which can accurately predict the indoor temperature. The accuracy of these models was measured by comparing their prediction to the actual indoor temperature

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration

Comfort-based Optimal Temperature Setpoint Calculation

General practice in current building HVAC control is to select building temperature setpoints that comply with ASHRAE Standard 55. By meeting this standard, based on the PMV comfort model, 80% of building occupants should be satisfied with their thermal environment. However, unfortunately, this is rarely the case. One possible reason for this is the variation in occupant activity and clothing that are usually assumed default values using this standard. In this work, we present an iterative-based algorithm to solve this problem. The algorithm solves the PMV inverse model equation to determine the optimal temperature setpoint while inferring human activity level from the biometric data of wearable fitness devices. The new algorithm is also designed to handle multi-occupants with conflicting comfort preferences scenario. Using this new algorithm, our results show a significant increase in occupant comfort, specifically when occupant activity is high

Comfort-based Optimal Temperature Setpoint Calculation

General practice in current building HVAC control is to select building temperature setpoints that comply with ASHRAE Standard 55. By meeting this standard, based on the PMV comfort model, 80% of building occupants should be satisfied with their thermal environment. However, unfortunately, this is rarely the case. One possible reason for this is the variation in occupant activity and clothing that are usually assumed default values using this standard. In this work, we present an iterative-based algorithm to solve this problem. The algorithm solves the PMV inverse model equation to determine the optimal temperature setpoint while inferring human activity level from the biometric data of wearable fitness devices. The new algorithm is also designed to handle multi-occupants with conflicting comfort preferences scenario. Using this new algorithm, our results show a significant increase in occupant comfort, specifically when occupant activity is high

Sensitivity Analysis for the PMV Thermal Comfort Model and the Use of Wearable Devices to Enhance Its Accuracy

This paper studies the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters. PMV model equations, adapted in the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to generate two-dimensional (2D) and three-dimensional (3D) comfort zone plots for different combinations of parameters. It is found that personal parameters such as clothing and metabolic rate, which are usually ignored or simply assumed to be constant values, have the highest impact. In this work, we demonstrate the use of smart wearable devices to estimate metabolic rate. The metabolic rate for an occupant during normal life activities is recorded using a Fitbit® wearable device. This example is used to do the following: (1) demonstrate the PMV expected error range when personal parameters are ignored, and (2) determine the potential of using a wearable device to enhance PMV comfort model accuracy

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration

An IoT Framework for Modeling and Controlling Thermal Comfort in Buildings

Humans spend more than 90% of their day in buildings, where their health and productivity are demonstrably linked to thermal comfort. Building thermal comfort systems account for the largest share of U.S energy consumption. Despite this high-energy cost, due to building design complexity and the variety of building occupant needs, addressing thermal comfort in buildings remains a difficult problem. To overcome this challenge, this paper presents an Internet of Things (IoT) approach to efficiently model and control comfort in buildings. In the model phase, a method to access and exploit wearable device data to build a personal thermal comfort model has been presented. Various supervised machine-learning algorithms are evaluated to produce accurate personal thermal comfort models for each building occupant that exhibit superior performance compared to a general model for all occupants. The developed comfort models were used to simulate an intelligent comfort controller that uses the particle swarm optimization(PSO) method to search for optimal control parameter values to achieve maximum comfort. Finally, a framework for experimental validation of the new proposed comfort controller that interactively works with the HVAC element has been introduced