Nothing Special   »   [go: up one dir, main page]

AlKandari et al., 2024 - Google Patents

Solar power generation forecasting using ensemble approach based on deep learning and statistical methods

AlKandari et al., 2024

View HTML
Document ID
13815621622603199278
Author
AlKandari M
Ahmad I
Publication year
Publication venue
Applied Computing and Informatics

External Links

Snippet

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that …
Continue reading at www.emerald.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management

Similar Documents

Publication Publication Date Title
AlKandari et al. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods
Mir et al. Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction
Kumari et al. Deep learning models for solar irradiance forecasting: A comprehensive review
Meenal et al. Weather forecasting for renewable energy system: a review
Mutavhatsindi et al. Forecasting hourly global horizontal solar irradiance in South Africa using machine learning models
Rana et al. Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling
Al-Dahidi et al. Assessment of artificial neural networks learning algorithms and training datasets for solar photovoltaic power production prediction
Buitrago et al. Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs
Torres et al. Deep learning for big data time series forecasting applied to solar power
Costa Convolutional-LSTM networks and generalization in forecasting of household photovoltaic generation
Belmahdi et al. Comparative optimization of global solar radiation forecasting using machine learning and time series models
Kang et al. Short‐Term Wind Speed Prediction Using EEMD‐LSSVM Model
Al-Rousan et al. Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods
Li et al. Short-term Forecasting Approach Based on bidirectional long short-term memory and convolutional neural network for Regional Photovoltaic Power Plants
Dhillon et al. A solar energy forecast model using neural networks: Application for prediction of power for wireless sensor networks in precision agriculture
Yildiz et al. A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant
Wang et al. Deep autoencoder with localized stochastic sensitivity for short-term load forecasting
Suresh et al. Probabilistic LSTM-Autoencoder based hour-ahead solar power forecasting model for intra-day electricity market participation: A Polish case study
Rajasundrapandiyanleebanon et al. Solar energy forecasting using machine learning and deep learning techniques
Shahid et al. Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory
Chen et al. Short-term wind power forecasting using mixed input feature-based cascade-connected artificial neural networks
Zhang et al. All‐factor short‐term photovoltaic output power forecast
Mellit An overview on the application of machine learning and deep learning for photovoltaic output power forecasting
Thangavelu et al. Forecasting of solar radiation for a cleaner environment using robust machine learning techniques
Brahma et al. Visualizing solar irradiance data in ArcGIS and forecasting based on a novel deep neural network mechanism