Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression

Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)

Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal operating voltages and currents, without relying on complex motor modeling or internal performance parameters not readily available

Forecasting Design Day Demand Using Extremal Quantile Regression

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day. We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques

Staying Connected – Interactive Student Learning during the COVID Transition to Remote Learning

Background. How can we transition courses in one week, while maintaining a similar experience for students? This was probably the initial response by faculty across universities as they transitioned to remote learning, mid-semester, in response to the SARS-CoV-2 pandemic. Our approach is supported by the ICAP framework which posits that “as activities move from passive to active to constructive to interactive, students undergo different knowledge-change processes and, as a result, learning will increase.” (Chi and Wylie, 2014) Purpose/Hypothesis. How we could foster students’ interactions with course material, instructors, and their peers using collaborative technology and course activities? It was hypothesized that a collaborative environment, coupled with appropriately designed activities, would promote the interactive learning described by the ICAP framework. Design/Method. Faculty members used Microsoft Teams (Teams) and Marquette University’s Learning Management System Desire2Learn (D2L) for their courses. Each instructor developed student groups to promote peer and instructor engagement via the Teams channel function. Results. Initial results from Likert 5-point scale responses support three positive findings to this approach: Finding 1 (Instructor Engagement and Student Confidence): Students had a positive reaction to the instructor engagement (4.67 ± 0.6) and student confidence (4.07 ± 1.1). Finding 2 (Consistent Coursework): Students reported the amount of work in courses with the interactive tools was consistent (3.90 ± 1.2) with the in-class experience. Finding 3 (Collaborative Technology): Using collaborative technology (3.84 ± 1.2) enabled the students to successfully interact with their peers. The survey also provided data on opportunities for improvement for future on-line courses: Opportunity 1 (Communication): Student communication (2.57 ± 1.5) is still a barrier with collaborative technology. Opportunity 2 (On-line Format): Students also reported an overall dislike (2.44 ± 1.4) of the on-line learning format. Conclusions. The use of Teams shows that instructor engagement contributes the most to the positive experiences for confidence, consistency, and use of collaborative technology. We believe there are opportunities to develop more advantages than traditional approaches and will provide students an easier transition to industry, which already use these remote communication tools

A Light Weight Smartphone Based Human Activity Recognition System with High Accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works

Time series data mining: Identifying temporal patterns for characterization and prediction of time series events

A new framework for analyzing time series data called Time Series Data Mining (TSDM) is introduced. This framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. Traditional time series analysis methods are limited by the requirement of stationarity of the time series and normality and independence of the residuals. Because they attempt to characterize and predict all time series observations, traditional time series analysis methods are unable to identify complex (nonperiodic, nonlinear, irregular, and chaotic) characteristics. TSDM methods overcome limitations of traditional time series analysis techniques. A brief historical review of related fields, including a discussion of the theoretical underpinnings for the TSDM framework, is made. The TSDM framework, concepts, and methods are explained in detail and applied to real-world time series from the engineering and financial domains