[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Jafri et al., 2023 - Google Patents

The role of artificial intelligence in solar harvesting, storage, and conversion

Jafri et al., 2023

Document ID
9632025200343934417
Author
Jafri N
Tahir M
Ahad A
Publication year
Publication venue
Solar Energy Harvesting, Conversion, and Storage

External Links

Snippet

Abstract With the United Nations (UN) push for sustainable development goals (SDGs), research on renewable energy resources has received significant renewed interest. Furthermore, major economies worldwide have committed to reducing carbon emissions …
Continue reading at www.sciencedirect.com (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
    • 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
    • G06N3/08Learning methods
    • 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
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/50Photovoltaic [PV] energy
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Wazirali et al. State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Han et al. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm
Voyant et al. Machine learning methods for solar radiation forecasting: A review
Zheng et al. Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies
Wang et al. Short-term wind speed forecasting based on information of neighboring wind farms
Mert Agnostic deep neural network approach to the estimation of hydrogen production for solar-powered systems
Khare et al. Solar energy system concept change from trending technology: A comprehensive review
Al-Rousan et al. Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods
Shareef et al. Random Forest‐Based Approach for Maximum Power Point Tracking of Photovoltaic Systems Operating under Actual Environmental Conditions
Wang et al. Deep autoencoder with localized stochastic sensitivity for short-term load forecasting
Saxena et al. Hybrid KNN-SVM machine learning approach for solar power forecasting
Das et al. Optimized support vector regression-based model for solar power generation forecasting on the basis of online weather reports
Souabi et al. Data-driven prediction models of photovoltaic energy for smart grid applications
Jafri et al. The role of artificial intelligence in solar harvesting, storage, and conversion
Farhadi et al. Machine learning for fast development of advanced energy materials
Goh et al. Hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction
Prema et al. LSTM based Deep Learning model for accurate wind speed prediction
Almalaq et al. Comparison of recursive and non-recursive ANNs in energy consumption forecasting in buildings
Khelifi et al. Machine learning for solar power systems-a short tour
Al Turki et al. Predicting the output power of a photovoltaic module using an optimized offline cascade-forward neural network-based on genetic algorithm model
Awais et al. Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism
Karatepe et al. Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells
Memarzadeh et al. A new hybrid intelligent method for accurate short term electric power production forecasting from uncertain renewable energy resources
Bhatti Machine learning for accelerating the discovery of high-performance low-cost solar cells
Shu et al. Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model