Publication:
Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems:

dc.contributor.author Harrou, Fouzi;Sun, Ying
dc.date.accessioned 2024-04-12T06:24:56Z
dc.date.available 2024-04-12T06:24:56Z
dc.date.issued 2020
dc.description Publisher: InTechOpen ; License: https://creativecommons.org/licenses/by-nc/4.0/legalcode ; Source: https://library.oapen.org/handle/20.500.12657/43847
dc.description.abstract Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.
dc.identifier.isbn 9781838805463
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/13351
dc.language.iso en_US
dc.subject Technology & Engineering
dc.subject Power Resources
dc.subject Alternative & Renewable
dc.title Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems:
dc.type Resource Types::text::book
dspace.entity.type Publication
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
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