【文章頁數(shù)】：104 頁

【學(xué)位級別】：博士

【文章目錄】：
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER 1 Introduction
    1.1 PV power schemes
        1.1.1 Off-grid PV scheme
        1.1.2 Grid-connected PV Schemes
    1.2 Global PV power industry
    1.3 PV power variations
    1.4 Motivation of thesis
    1.5 Objectives of thesis
    1.6 Frame work of Thesis
    1.7 Outline of Thesis
CHAPTER 2 Solar radiation measurement and PV power forecasting methods
    2.1 Solar Radiation Elements at the Earth Level
    2.2 Solar Radiation measurements
        2.2.1 Satellite Based Models for measuring Radiation
        2.2.2 Online Databases for radiation measurement
    2.3 PV power plant scheme installation
    2.4 Global horizontal and clear sky radiation
    2.5 Cloud Motion Vector (CMV) predictions of radiation
    2.6 Datasets for irradiance forecast analysis
    2.7 CMV forecasts of irradiance by cloud index
    2.8 Forecasting methods of PV power
    2.9 Regressive methods
        2.9.1 Linear stationary models
        2.9.2 Auto-Regressive (AR) models
        2.9.3 Moving Average (MA) models
        2.9.4 Mixed Auto-Regressive Moving Average (ARMA) models
        2.9.5 Mixed Auto-Regressive Moving Average models withexogenous variables (ARMAX)
    2.10 Machine learning methods
        2.10.1 Linear regression
        2.10.2 Generalized linear models
        2.10.3 Nonlinear regression
        2.10.4 Support vector machines/support vector regression
        2.10.5 Decision tree learning (Breiman bagging)
        2.10.6 Nearest neighbor
        2.10.7 Markov chain
        2.10.8 Unsupervised learning
        2.10.9 K-means and k-methods clustering
        2.10.10 Hierarchical clustering
CHAPTER 3 Analysis and review of PV power
    3.1 Study of PV Power Data
    3.2 Relationship Between meteorological parameters and PV Output
        3.2.1 Numerical weather radiation
        3.2.2 Ambient Temperature
        3.2.3 Wind Speed
        3.2.4 Humidity
        3.2.5 Fog
    3.3 Review of forecasting methods
        3.3.1 Solar irradiance forecasting review
        3.3.2 PV Power Forecasting Review
CHAPTER 4 PV power forecast in haze weather
    4.1 Forecasting of PV power in haze weather
    4.2 Haze weather in Beijing
    4.3 Effect of Haze on PV power
    4.4 Effect of meteorological parameters on PV power
    4.5 BP Network
        4.5.1 Fundamentals of BP Network
        4.5.2 Hybrid forecasting method
    4.6 Simulation Results
        4.6.1 Haze Weather or high AQI Tests
        4.6.2 Clear weather Tests
CHAPTER 5 PV power forecast during normal weather
    5.1 PV power forecasting
    5.2 Effect of weather data on PV power
    5.3 Cascade feed forward network
        5.3.1 PV power forecasting by Cascade feed forward network
        5.3.2 Simulation results of CF network
    5.4 Forecasting with Elman neural network
        5.4.1 Algorithm of Elman network
        5.4.2 Results of Elman neural network
    5.5 Conclusion
CHAPTER 6 Conclusion
    6.1 Contributions
    6.2 Guidelines for Future Work
References
Biography



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