Power Quality daily Predictions in Smart Off-grids using Differential, Deep and Statistics Machine Learning models processing NWP-data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252107" target="_blank" >RIV/61989100:27240/23:10252107 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.esr.2023.101076" target="_blank" >https://doi.org/10.1016/j.esr.2023.101076</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.esr.2023.101076" target="_blank" >10.1016/j.esr.2023.101076</a>

Result language

angličtina

Original language name

Power Quality daily Predictions in Smart Off-grids using Differential, Deep and Statistics Machine Learning models processing NWP-data

Original language description

Autonomous microgrid networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilisation of renewable energy (RE) is unavoidable to optimise the performance of the system without abnormalities. Alterations and irregularities in PQ must remain within the prescribed norm ranges and characteristics to allow fault-tolerant operation of the detached system in various modes of attached equipment. The PQ data for all possible combinations of grid-attached household appliances and different inside/outside conditions cannot be completely measured or described exactly by physical equations. PQ predictions on a daily basis using Artificial Intelligence (AI) models are needed because atmospheric fluctuations and anomalies in local weather with uncertainties in system states primarily influence the induced power and operation of real off-grids. A novel soft-computing method using Differential Learning, which allows modelling of complex dynamics of weather-dependent systems, is presented and compared with the recent standard deep and probabilistic machine learning. AI models were developed using weather data and the binary status of the attached equipment during the predetermined daily training periods of the test. Daily statistical models process 24-hour forecast data and definition load series of trained input variables to calculate the target PQ parameters at the same times. Optimal utilisation, efficiency, and failure-free operation of smart grids can be planned according to the suggested operable power consumption scenarios based on their PQ verification on a day-horizon. Executable load sequences can be automatically combined and scheduled in the system to be adapted to user needs, considering the RE production potential, charge state, and optimal PQ characteristics over the next 24 hours. Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of the results.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

Energy Strategy Reviews

ISSN

2211-467X

e-ISSN

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Volume of the periodical

47

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

1-13

UT code for WoS article

000966684100001

EID of the result in the Scopus database

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