Shendryk et al., 2022 - Google Patents
Short-term Solar Power Generation Forecasting for MicrogridShendryk et al., 2022
- Document ID
- 2628284398797518
- Author
- Shendryk V
- Parfenenko Y
- Kholiavka Y
- Pavlenko P
- Shendryk O
- Bratushka L
- Publication year
- Publication venue
- 2022 IEEE 3rd International Conference on System Analysis & Intelligent Computing (SAIC)
External Links
Snippet
Nowadays, the world's energy consumption is growing, and solving the problem of replacing traditional sources with alternative ones is urgent. The solution to this problem is impossible without prior data analysis and further forecasting of energy production from alternative …
- 238000010248 power generation 0 title abstract description 16
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- 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
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/54—Management of operational aspects, e.g. planning, load or production forecast, maintenance, construction, extension
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Communication or information technology specific aspects supporting electrical power generation, transmission, distribution or end-user application management
- Y04S40/20—Information technology specific aspects
- Y04S40/22—Computer aided design [CAD]; Simulation; Modelling
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Madhukumar et al. | Regression model-based short-term load forecasting for university campus load | |
Behera et al. | Solar photovoltaic power forecasting using optimized modified extreme learning machine technique | |
Mahmoud et al. | An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine | |
Du et al. | Multi-step ahead forecasting in electrical power system using a hybrid forecasting system | |
Chang et al. | An improved neural network-based approach for short-term wind speed and power forecast | |
Marín et al. | Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks | |
Bassey | Hybrid renewable energy systems modeling | |
Raza et al. | Multivariate ensemble forecast framework for demand prediction of anomalous days | |
Zhu et al. | Short‐Term Electricity Consumption Forecasting Based on the EMD‐Fbprophet‐LSTM Method | |
Jurj et al. | Overview of electrical energy forecasting methods and models in renewable energy | |
Zhao et al. | Short-term wind electric power forecasting using a novel multi-stage intelligent algorithm | |
Cruz et al. | Neural network prediction interval based on joint supervision | |
Ahmad et al. | Efficient energy planning with decomposition-based evolutionary neural networks | |
Alharbi et al. | Short-term wind speed and temperature forecasting model based on gated recurrent unit neural networks | |
Pandu et al. | Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts. | |
Eseye et al. | Short-term forecasting of electricity consumption in buildings for efficient and optimal distributed energy management | |
Zahraoui et al. | ANN-LSTM Based Tool For Photovoltaic Power Forecasting. | |
Famoso et al. | A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant | |
Almeida et al. | Hierarchical time series forecast in electrical grids | |
Shendryk et al. | Short-term Solar Power Generation Forecasting for Microgrid | |
Baltputnis et al. | ANN-based city heat demand forecast | |
Bahij et al. | A review on the prediction of energy consumption in the industry sector based on machine learning approaches | |
Neudakhina et al. | An ANN-based intelligent system for forecasting monthly electric energy consumption | |
Ding et al. | A statistical upscaling approach of region wind power forecasting based on combination model | |
Gilbert et al. | A hierarchical approach to probabilistic wind power forecasting |