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Deep Neural Network in Heat Demand Analysis

ABSTRACT第5页
摘要第6-7页
ACKNOWLEDGEMENTS第7-12页
CHAPTER 1: INTRODUCTION第12-23页
    1.1 Research background and significance第12-16页
    1.2 Research status第16-20页
    1.3 Research content and key technologies第20-21页
    1.4 Thesis structure arrangement第21-23页
CHAPTER 2: Fuzzy Neural Network Model for Heating Load Forecasting第23-38页
    2.1 The basic characteristics of heating load forecasting第23-24页
    2.2 Analysis of Fuzzy Logic System第24-26页
        2.2.1 The Development and Research of Fuzzy Technology第24页
        2.2.2 Introduction to fuzzy systems第24-26页
    2.3 Analysis of Artificial Neural Network第26-32页
        2.3.1 Characteristics of Artificial Neural Networks第26-28页
        2.3.2 Principle of Artificial Neural Networks第28-29页
        2.3.3 Principle of BP neural network第29-30页
        2.3.4 Calculation process of BP neural network第30-32页
    2.4 The Combination of Fuzzy Logic and Neural Network第32-37页
        2.4.1 Knowledge Processing of Fuzzy Systems and Neural Networks第32-33页
        2.4.2 The Function Equivalence of Fuzzy System and Neural Network第33-34页
        2.4.3 The Necessity of Combining Fuzzy System with Neural Network第34-36页
        2.4.4 Advantages of Fuzzy Neural Network in Short-term Load Forecasting第36-37页
    2.5 Chapter summary第37-38页
CHAPTER 3: Design of Fuzzy Neural Network for Heating Load Forecasting第38-48页
    3.1 The basic idea of heating load forecasting第38页
    3.2 The basic process of load forecasting第38-40页
    3.3 Selection of input and output variables for load forecasting fuzzy neural network model第40-43页
        3.3.1 Select the input variable第40-42页
        3.3.2 Select the output variable第42-43页
    3.4 The Structure of Fuzzy Neural Network for Centralized Heat Load Forecasting第43-45页
    3.5 The learning steps of fuzzy neural network第45-46页
    3.6 Chapter summary第46-48页
CHAPTER 4: Carry on the simulation test to the heating load forecasting model第48-57页
    4.1 Data description第48-49页
    4.2 Historical load data and preprocessing of input and output parameters第49-50页
        4.2.1 Preprocessing of historical load data第49页
        4.2.2 Preprocessing of input and output parameters第49-50页
    4.3 Result Analysis Based on Fuzzy Neural Network第50-56页
        4.3.1 Initialization of network training parameters第50-54页
        4.3.2 Predictive Results of Fuzzy Neural Networks第54-56页
    4.4 Chapter summary第56-57页
CHAPTER 5: Summary and outlook第57-59页
    5.1 Work summary第57页
    5.2 Looking ahead第57-59页
REFERENCES第59-61页

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