All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Power quality 24-hour prediction using differential, deep and statistics machine learning based on weather data in an off-grid

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10250150" target="_blank" >RIV/61989100:27240/23:10250150 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.sciencedirect.com/science/article/pii/S0016003222004586" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0016003222004586</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.jfranklin.2022.06.048" target="_blank" >10.1016/j.jfranklin.2022.06.048</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Power quality 24-hour prediction using differential, deep and statistics machine learning based on weather data in an off-grid

  • Original language description

    Prediction of Power Quality (PQ) on a daily basis is inevitable in planning Renewable Energy (RE) supply and scheduling the assumed power load in smart off-grid autonomous systems. Various combinations of the attached household appliances lead to specific operating states, related to the charge and load switching-time in different out-side conditions. Complexity and irregularities require modelling with the use of Artificial Intelligence (AI) to represent the uncertainty in load transitions and RE power oscillations. These specifics cannot be numerically solved with respect to unexpected states in detached micro-grid systems showing great variability. Recent Differential Learning, developed by the author, using a novel designed neuro-computing approach, enables one to model high non-linear and indefinable chaotic physical systems. The optimal training periods of day-set records were initially pre-assessed, based on AI input-output statistics. This model initialization allows operable day-ahead PQ-predictions in processing the last 24-hour series in one sequence. The proposed 2-level PQ-management evaluates initial load scheduling plans, based on available RE and storage sources. System efficiency and failure-free operations are planned considering the first estimate of its day-ahead PVP supply and secondary PQ verification of the provided load utilization schemes, under various state charges and RE production limits. This is a novelty with a notable incremental improvement in the published combinatorial optimization algorithms, only scheduling applicable load components, and reassessing the allowable power-day resources. Off-grid optimal operations can be determined and regulated by an adequate early morning PQ evaluation in RE utilization. A C++ parametric software, using differential learning to evolve PQ-models, historical PQ &amp; meteo-data sets are at disposal to enable additional comparisons with the presented models. (C) 2022 The Franklin Institute

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Journal of the Franklin Institute - Engineering and Applied Mathematics

  • ISSN

    0016-0032

  • e-ISSN

  • Volume of the periodical

    360

  • Issue of the periodical within the volume

    17

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    25

  • Pages from-to

    13712-13736

  • UT code for WoS article

    000000000000000

  • EID of the result in the Scopus database

    2-s2.0-85135356206