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

Rangelov et al., 2023 - Google Patents

Design and development of a short-term photovoltaic power output forecasting method based on Random Forest, Deep Neural Network and LSTM using readily …

Rangelov et al., 2023

View PDF
Document ID
9217955364430612944
Author
Rangelov D
Boerger M
Tcholtchev N
Lämmel P
Hauswirth M
Publication year
Publication venue
IEEE Access

External Links

Snippet

Renewable energy sources (RES) are an essential part of building a more sustainable future, with higher diversity of clean energy, reduced emissions and less dependence on finite fossil fuels such as coal, oil and natural gas. The advancements in the renewable …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting

Similar Documents

Publication Publication Date Title
Gaboitaolelwe et al. Machine learning based solar photovoltaic power forecasting: a review and comparison
Wang et al. Short-term wind speed forecasting based on information of neighboring wind farms
Voyant et al. Machine learning methods for solar radiation forecasting: A review
Ghimire et al. Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms
Ghimire et al. Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia
Rangelov et al. Design and development of a short-term photovoltaic power output forecasting method based on Random Forest, Deep Neural Network and LSTM using readily available weather features
Mellit et al. Least squares support vector machine for short-term prediction of meteorological time series
Jiang et al. Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation
Alrashidi et al. Global solar radiation prediction: Application of novel hybrid data-driven model
Dhillon et al. A solar energy forecast model using neural networks: Application for prediction of power for wireless sensor networks in precision agriculture
Wang et al. Static and dynamic ensembles of neural networks for solar power forecasting
Boutahir et al. Machine learning and deep learning applications for solar radiation predictions review: morocco as a case of study
Liu et al. Short-term photovoltaic power forecasting with feature extraction and attention mechanisms
Salman et al. A GMEE-WFED System: Optimizing Wind Turbine Distribution for Enhanced Renewable Energy Generation in the Future
Singh et al. Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning
Ahmed et al. Investigating boosting techniques’ efficacy in feature selection: A comparative analysis
Sammar et al. Illuminating the Future: A Comprehensive Review of AI-Based Solar Irradiance Prediction Models
Wang et al. Solar power forecasting using dynamic meta-learning ensemble of neural networks
Hissou et al. A lightweight time series method for prediction of solar radiation
Yeboah et al. PREDICTING SOLAR RADIATION FOR RENEWABLE ENERGY TECHNOLOGIES: ARandom FOREST APPROACH
Alghazo et al. Prediction of the Performance of a Sun Tracking Photovoltaic System using different Artificial Intelligence Techniques: Case Study in Zarqa, Jordan
Nadeem et al. AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning
Gupta et al. A review of the state of the art in solar photovoltaic output power forecasting using data-driven models
Sahani et al. Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network
Sua et al. Predicting Power Output of Solar Panels Using Machine Learning Algorithms