Predictive Analytics in Future Power Systems: A Panorama and State-Of-The-Art of Deep Learning Applications

Mishra, Sakshi; Glaws, Andrew; Palanisamy, P.

doi:10.1007/978-3-030-34094-0_7

Cited by 4 publications

(3 citation statements)

References 70 publications

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“…Finally, utility regulators can use predictive analytics to create precise financial models to consider the costs and benefits of future deployments [27,28]. Accurate demand forecasting is crucial to perform efficient energy planning and trading.…”

Section: Technical Solutions For Energy Efficiency With Sgs and Homesmentioning

confidence: 99%

A Survey on Energy Efficiency in Smart Homes and Smart Grids

et al. 2021

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Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGs.

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Section: Technical Solutions For Energy Efficiency With Sgs and Homesmentioning

confidence: 99%

A Survey on Energy Efficiency in Smart Homes and Smart Grids

et al. 2021

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“…These applications range from image recognition, object detection, power systems, breast cancer detection, speech recognition to drug discovery and genomics, etc. [ 114 , 115 , 116 , 117 ]. In the following sections, deep learning models for breast cancer diagnosis are presented.…”

Section: Computational Techniques Used In Breast Cancer Imaging Diagnosticsmentioning

confidence: 99%

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

et al. 2020

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With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.

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“…There are numerous advanced applications of predictive analytics in the renewable energy field [20]-spanning the energy system from generation (solar and wind forecasting) to consumption (smart buildings energy forecasting to fault predictions). Because the focus of this article is smart buildings' predictive analytics application, herein we discuss literary works focused on data-driven building load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based, Multi-Timescale Load Forecasting in Buildings: Opportunities and Challenges from Research to De-ployment

Mishra¹,

Frank²,

Petersen³

et al. 2022

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Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.

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Predictive Analytics in Future Power Systems: A Panorama and State-Of-The-Art of Deep Learning Applications

Cited by 4 publications

References 70 publications

A Survey on Energy Efficiency in Smart Homes and Smart Grids

A Survey on Energy Efficiency in Smart Homes and Smart Grids

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

Deep-Learning-Based, Multi-Timescale Load Forecasting in Buildings: Opportunities and Challenges from Research to De-ployment

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