Engineering Commons^™

Digital Twin For Hvac Load And Energy Storage Based On A Hybrid Ml Model With Cta-2045 Controls Capability, Rosemary E. Alden, Evan S. Jones, Huangjie Gong, Abdullah Al Hadi, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to widespread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing …

Forecast Of Community Total Electric Load And Hvac Component Disaggregation Through A New Lstm-Based Method, Huangjie Gong, Rosemary E. Alden, Aron Patrick, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling …

Explainable Data-Driven Motor Condition Monitoring And Fault Disgnosis, Yuming Wang

Theses and Dissertations--Electrical and Computer Engineering

Industrial motors are widely used in various fields such as power generation, mining, and manufacturing. Motor faults and time-consuming maintenance process will lead to serious economic losses in this context. To monitor motor faults and detect motor conditions, different types of sensors that can test vibration and current signals are mounted on motors. However, the main challenge was how to use information gained by sensors to analyze or diagnose motor conditions.

Machine learning is a popular technology in recent years, and it's very suitable for crunching and analyzing data. As an important subset of machine learning, deep learning is suitable …

Nondestructive Detection Of Codling Moth Infestation In Apples Using Pixel-Based Nir Hyperspectral Imaging With Machine Learning And Feature Selection, Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva, Akinbode A. Adedeji

Electrical and Computer Engineering Faculty Publications

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing …

Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In …

Intelligent Sensors For Sustainable Food And Drink Manufacturing, Nicholas J. Watson, Alexander L. Bowler, Ahmed Rady, Oliver J. Fisher, Alessandro Simeone, Josep Escrig, Elliot Woolley, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology …

Machine Learning Approach For Vigilance State Classification In Mice, Anik Muhury

Theses and Dissertations--Electrical and Computer Engineering

Sleep has a significant impact on cognitive abilities such as memory, reaction time, productivity, and creative thinking; however, there are many aspects of this important activity that are not clearly understood. Over the last century, researchers have developed technology and animal models to assist in the study of sleep. Manual sleep scoring is time consuming, reduces productivity, and is impacted by human scorer subjectivity. On the other hand, automatic sleep stage categorization can enhance consistency and reliability, aiding professionals in identifying sleep related health problems.

In recent times various studies reported significant achievements for automatic vigilance detection and overcome the …

Lstm Forecasts For Smart Home Electricity Usage, Rosemary E. Alden, Huangjie Gong, Cristinel Ababei, Dan M. Ionel

Power and Energy Institute of Kentucky Faculty Publications

With increasing of distributed energy resources deployment behind-the-meter and of the power system levels, more attention is being placed on electric load and generation forecasting or prediction for individual residences. While prediction with machine learning based approaches of aggregated power load, at the substation or community levels, has been relatively successful, the problem of prediction of power of individual houses remains a largely open problem. This problem is harder due to the increased variability and uncertainty in user consumption behavior, which make individual residence power traces be more erratic and less predictable. In this paper, we present an investigation of …

Novel Applications Of Machine Learning In Bioinformatics, Yi Zhang

Theses and Dissertations--Computer Science

Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms.

A critical step in defining gene structures and mRNA …

Weld Penetration Identification Based On Convolutional Neural Network, Chao Li

Theses and Dissertations--Electrical and Computer Engineering

Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms.

A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern …