Energy consumers may not know whether their next-hour forecasted load is either high or low based on the actual value predicted from their historical data. A conventional method of level prediction with a pattern recognition approach was performed by first predicting the actual numerical values using typical pattern-based regression models, hen classifying them into pattern levels (e.g., low, average, and high). A proposed prediction with pattern recognition scheme was developed to directly predict the desired levels using simpler classifier models without undergoing regression. The proposed pattern recognition classifier was compared to its regression method using a similar algorithm applied to a real-world energy dataset. A random forest (RF) algorithm which outperformed other widely used machine learning (ML) techniques in previous research was used in both methods. Both schemes used similar parameters for training and testing simulations. After 10-time cross training validation and five averaged repeated runs with random permutation per data splitting, the proposed classifier shows better computation speed and higher classification accuracy than the conventional method. However, when the number of its desired levels increases, its prediction accuracy seems to decrease and approaches the accuracy of the conventional method. The developed energy level prediction, which is computationally inexpensive and has a good classification performance, can serve as an alternative forecasting scheme.

A novel motor fault diagnosis using only motor current signature is developed using a frequency occurrence plot-based convolutional neural network (FOP-CNN). In this study, a healthy motor and four identical motors with synthetically applied fault conditions—bearing axis deviation, stator coil inter-turn short circuiting, a broken rotor strip, and outer bearing ring damage—are tested. A set of 150 three-second sampling stator current signals from each motor fault condition are taken under five artificial coupling loads (0, 25%, 50%, 75% and 100%). The sampling signals are collected and processed into frequency occurrence plots (FOPs) which later serve as CNN inputs. This is done first by transforming the time series signals into its frequency spectra then convert these into two-dimensional FOPs. Fivefold stratified sampling cross-validation is performed. When motor load variations are considered as input labels, FOP-CNN predicts motor fault conditions with a 92.37% classification accuracy. It precisely classifies and recalls bearing axis deviation fault and healthy conditions with 99.92% and 96.13% f-scores, respectively. When motor loading variations are not used as input data labels, FOP-CNN still satisfactorily predicts motor condition with an 80.25% overall accuracy. FOP-CNN serves as a new feature extraction technique for time series input signals such as vibration sensors, thermocouples, and acoustics.

Traffic congestion is an undesirable problem for big cities especially in third world countries. Better policy planning and decision-making from the authority comes from well-conducted practical research. In this study, a Vehicle Count System (VCS) using deep learning Mask R-CNN is developed to classify and count vehicles passing in a target street. A novel time interval image capture (TIIC) system is employed to the VCS instead of the typical real-time video streaming to avoid big data storage cost. To determine the effectiveness of the developed VCS, its output is compared to that of conventional method from manual recording of the passing vehicles. Four vehicle types – cars, motorbikes, trucks and buses are present in the 1800 real traffic images gathered from an actual field. As an initial stage, the developed tool performs satisfactorily in classifying and counting car-type vehicle with 97.62% accuracy in a 10-hour testing. However, it fails to recognize motorbikes probably due to its relatively smaller pixel size compared to other vehicle types. The presence of jeeps confused the VCS. The real image dataset can be used as basis for further development. The newly developed TIIC system can also be used in future research as a promising tool to replace real-time video streaming.