About: Prediction of Photovoltaic Power Using Nature-Inspired Computing

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Prediction of Photovoltaic Power Using Nature-Inspired Computing Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Prediction of Photovoltaic Power Using Nature-Inspired Computing
Creator	Ezzeddine, Anna Grmanová, Gabriela Rozinajová, Viera Sumega, Miroslav
source	PMC
abstract	Prediction of photovoltaic (PV) energy is an important task. It allows grid operators to plan production of energy in order to secure stability of electrical grid. In this work we focus on improving prediction of PV energy using nature-inspired algorithms for optimization of Support Vector Regression (SVR) models. We propose method, which uses different models optimized for various types of weather in order to achieve higher overall accuracy compared to single optimized model. Each sample is classified by Multi-Layer Perceptron (MLP) into some weather class and then model is trained for each weather class. Our method achieved slightly better results compared to single optimized model.
has issue date	2020-06-22(xsd:dateTime)
bibo:doi	10.1007/978-3-030-53956-6_3
has license	no-cc
sha1sum (hex)	9a90e66685b77e53db79d9c6b0b0c532e14d0979
schema:url	https://doi.org/10.1007/978-3-030-53956-6_3
resource representing a document's title	Prediction of Photovoltaic Power Using Nature-Inspired Computing
has PubMed Central identifier	PMC7354785
schema:publication	Advances in Swarm Intelligence
resource representing a document's body	covid:9a90e66685b77e53db79d9c6b0b0c532e14d0979#body_text
is schema:about of	named entity 'weather' named entity 'energy' named entity 'Power' named entity 'Each' named entity 'model' named entity 'Regression' named entity 'grid operators' named entity 'focus' named entity 'energy' named entity 'SVR' named entity 'classified' named entity 'types' named entity 'order' named entity 'optimized' named entity 'order' named entity 'MLP' named entity 'SVR' named entity 'electrical grid' named entity 'model' named entity 'random movement' named entity 'SVR' named entity 'RBF kernel' named entity 'MBE' named entity 'multiclass' named entity 'MAE' named entity 'training data set' named entity 'power production' named entity 'SVR' named entity 'probability' named entity 'kernel functions' named entity 'training data set' named entity 'algorithm' named entity 'renewable energy' named entity 'RMSE' named entity 'hyperparameters' named entity 'real values' named entity 'total energy' named entity 'RMSE' named entity 'SVR' named entity 'normalized' named entity 'SVR' named entity 'power production' named entity 'classifier' named entity 'time intervals' named entity 'MAE' named entity 'RMSE' named entity 'hyperparameters' named entity 'kNN' named entity 'SVR' named entity 'SVR' named entity 'power production' named entity 'MAE' named entity 'optimization algorithms' named entity 'hyperparameter' named entity 'Root Mean Squared Error' named entity 'scattering' named entity 'multiclass' named entity 'Support Vector Machine' named entity 'RMSE' named entity 'Bias Error' named entity 'air temperature' named entity 'SVR' named entity 'feature space' named entity 'SVR' named entity 'vector' named entity 'vector' named entity 'MLP' named entity 'SVR' named entity 'data points' named entity 'nature-inspired' named entity 'hyperparameter' named entity '2014 and 2015' named entity 'RMSE' named entity 'neural networks' named entity 'University of Queensland' named entity 'RMSE' named entity 'random movement' named entity 'local minima' named entity 'SVM' named entity 'Firefly algorithm'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software