447 research outputs found
Efficient use of deep learning and machine learning for load forecasting in South African power distribution networks
Abstract: Load forecasting, which is the act of anticipating future loads, has been shown to be important in power system network planning, operations and maintenance. Artificial Intelligence (AI) techniques have been shown to be good tools for load forecasting. Load forecasting can assist power distribution utilities maximise their revenue through optimising maintenance planning. With the dawn of the smart grid, first world countries have moved past the customer’s point of supply and use smart meters to forecast customer loads. These recent studies also utilise recent state of the art AI techniques such as deep learning techniques. Weather parameters are such as temperature, humidity and rainfall are usually used as parameters in these studies. South African load forecasting studies are outdated and recent studies are limited. Most of these studies are from 2010, and dating backwards to 1999. Hence they do not use recent state of the art AI techniques. The studies do not focus at distribution level load forecasting for optimal maintenance planning. The impact of adjusting power consumption data when there are spikes and dips in the data was not investigated in all these South African studies. These studies did not investigate the impact of weather parameters on different South African loads and hence load forecasting performance...D.Phil. (Electrical and Electronic Management
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
Aeronautical Engineering. A continuing bibliography with indexes, supplement 156
This bibliography lists 288 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1982
Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4
Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences
Aeronautical engineering: A continuing bibliography with indexes, supplement 100
This bibliography lists 295 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in August 1978
Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications
This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators
Artificial Intelligence for the Detection of Electricity Theft and Irregular Power Usage in Emerging Markets
Power grids are critical infrastructure assets that face non-technical losses (NTL), which include, but are not limited to, electricity theft, broken or malfunctioning meters and arranged false meter readings. In emerging markets, NTL are a prime concern and often range up to 40% of the total electricity distributed. The annual world-wide costs for utilities due to NTL are estimated to be around USD 100 billion. Reducing NTL in order to increase revenue, profit and reliability of the grid is therefore of vital interest to utilities and authorities. In the beginning of this thesis, we provide an in-depth discussion of the causes of NTL and the economic effects thereof. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electric utilities are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data. This is due to the latter's propensity to suggest a large number of unnecessary inspections. In this thesis, we compare expert knowledge-based decision making systems to automated statistical decision making. We then branch out our research into different directions: First, in order to allow human experts to feed their knowledge in the decision process, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. Second, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data as well as a selection of master data. The methodology used is specifically tailored to the level of noise in the data. Last, we discuss the issue of biases in data sets. A bias occurs whenever training sets are not representative of the test data, which results in unreliable models. We show how quantifying and reducing these biases leads to an increased accuracy of the trained NTL detectors. This thesis has resulted in appreciable results on real-world big data sets of millions customers. Our systems are being deployed in a commercial NTL detection software. We also provide suggestions on how to further reduce NTL by not only carrying out inspections, but by implementing market reforms, increasing efficiency in the organization of utilities and improving communication between utilities, authorities and customers
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