Search Results (4)

In order to reduce the cost of data transmission, the meter data management system (MDMS) of the power operator usually delays time to obtain the power generation information of a solar photovoltaic (PV) power generation system. Although this approach solves the problem of data transmission cost, it brings more challenges to the solar PV power generation forecast. Because power operators usually need real-time solar PV power generation as a basis for the power dispatch, but considering the cost of communication, they cannot always provide corresponding historical power generation data in real time. In this study, an intelligent solar PV power generation forecasting mechanism combined with weather information is designed to cope with the issue of the absence of real-time power generation data. Firstly, the Pearson correlation coefficient analysis is used to find major factors with a high correlation in relation to solar PV power generation to reduce the computational burden of data fitting via a deep neural network (DNN). Then, the data preprocessing, including the standardization and the anti-standardization, is adopted for data-fitting or real-time solar PV power generation data to take as the input data of a long short-term memory neural network (LSTM). The salient features of the proposed DNN-LSTM model are: (1) only the information of present solar PV power generation is required to forecast the one at the next instant, and (2) an on-line learning mechanism is helpful to adjust the trained model to adapt different solar power plant or environmental conditions. In addition, the effectiveness of the trained model is verified by six actual solar power plants in Taiwan, and the superiority of the proposed DNN-LSTM model is compared with other forecasting models. Experimental verifications show that the proposed forecasting model can achieve a high accuracy of over 97%. Full article

Currents’ Physical Components (CPC) theory with spectral component representation is proposed as a generic grid interpretation method for detecting variations and structures. It is shown theoretically and validated experimentally that scattered and reactive CPC currents are highly suited for anomaly detection. CPC are enhanced by recursively disassembling the currents into 6 scattered subcomponents and 22 subcomponents overall, where additional anomalies dominate the subcurrents. Further disassembly is useful for anomaly detection and for grid deciphering. It is shown that the newly introduced syntax is highly effective for identifying variations even when the detected signals are in the order of 10⁻³ compared to conventional methods. The admittance physical components’ transfer functions, Y_i(ω), have been shown to improve the physical sensory function. The approach is exemplified in two scenarios demonstrating much higher sensitivity than classical electrical measurements. The proposed module may be located at a data center remote from the sensor. The CPC preprocessor, by means of a deep learning CNN, is compared to the current FFT and the current input raw data, which demonstrates 18% improved accuracy over FFT and 45% improved accuracy over raw current i(t). It is shown that the new preprocessor/detector enables highly accurate anomaly detection with the CNN classification core. Full article

In systems connected to smart grids, smart meters with fast and efficient responses are very helpful in detecting anomalies in realtime. However, sending data with a frequency of a minute or less is not normal with today’s technology because of the bottleneck of the communication network and storage media. Because mitigation cannot be done in realtime, we propose prediction techniques using Deep Neural Network (DNN), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). In addition to these techniques, the prediction timestep is chosen per day and wrapped in sliding windows, and clustering using Kmeans and intersection Kmeans and HDBSCAN is also evaluated. The predictive ability applied here is to predict whether anomalies in electricity usage will occur in the next few weeks. The aim is to give the user time to check their usage and from the utility side, whether it is necessary to prepare a sufficient supply. We also propose the latency reduction to counter higher latency as in the traditional centralized system by adding layer Edge Meter Data Management System (MDMS) and Cloud-MDMS as the inference and training model. Based on the experiments when running in the Raspberry Pi, the best solution is choosing DNN that has the shortest latency 1.25 ms, 159 kB persistent file size, and at 128 timesteps. Full article

Conventional outpatient services are unlikely to meet burgeoning demand for diabetes services given increasing prevalence of diabetes, and resultant impact on the healthcare workforce and healthcare costs. Disruptive technologies (such as smartphone and wireless sensors) create an opportunity to redesign outpatient services. In collaboration, the Department of Diabetes and Endocrinology at Brisbane Princess Alexandra Hospital, the University of Queensland Centre for Health Services Research and the Australian e-Health Research Centre developed a mobile diabetes management system (MDMS) to support the management of complex outpatient type 2 diabetes mellitus (T2DM) adults. The system comprises of a mobile App, an automated text-messaging feedback and a clinician portal. Blood glucose levels (BGL) data are automatically transferred by Bluetooth-enabled glucose meter to the clinician portal via the mobile App. The primary aim of the study described here is to examine improvement in glycaemic control of a new model of care employing MDMS for patients with complex T2DM attending a tertiary level outpatient service. A two-group, 12-month, pilot pragmatic randomised control trial will recruit 44 T2DM patients. The control group will receive routine care. The intervention group will be supported by the MDMS enabling the participants to potentially better self-manage their diabetes, and the endocrinologists to remotely monitor BGL and to interact with patients through a variety of eHealth modalities. Intervention participants will be encouraged to complete relevant pathology tests, and report on current diabetes management through an online questionnaire. Using this information, the endocrinologist may choose to reschedule the appointment or substitute it with a telephone or video-consultation. This pilot study will guide the conduct of a large-scale study regarding the capacity for a new model of care. This model utilises multimodal eHealth strategies via the MDMS to primarily improve glycaemic control with secondary aims to improve patient experience, reduce reliance on physical clinics, and decrease service delivery cost. Full article