Jafri et al., 2023 - Google Patents
The role of artificial intelligence in solar harvesting, storage, and conversionJafri 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 …
Classifications
-
- 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
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive 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
- G05B13/027—Adaptive 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
-
- Y—GENERAL 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive 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
-
- Y—GENERAL 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer 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 |