Improving the Accuracy of Short-term Forecasting of Electric
發(fā)布時(shí)間：2021-07-05 16:53
目前,國內(nèi)外研發(fā)了許多種負(fù)荷預(yù)測模型和軟件系統(tǒng)。多數(shù)模型的預(yù)測精度能夠滿足電力系統(tǒng)調(diào)度與用戶需求,但在某些情況下,短期負(fù)荷預(yù)測結(jié)果并不總是很理想。因此,結(jié)合當(dāng)?shù)貧庀髼l件和自然光照的預(yù)測模型迫切需要發(fā)展。主要研究內(nèi)容如下:（1）基于支持向量機(jī)和粒子群的算法,一個(gè)用于地區(qū)調(diào)度的短期電力系統(tǒng)負(fù)荷預(yù)測模型被建立。該方法將當(dāng)?shù)刈匀还庾鳛橛绊戭A(yù)測精度的一個(gè)重要因素,因此可以提高預(yù)測精確度。（2）粒子群算法被用于優(yōu)化支持向量回歸模型的參數(shù),其中空氣溫度和自然光照考慮作為影響因素。（3）通過對神經(jīng)模糊網(wǎng)絡(luò)和支持矢量機(jī)的預(yù)測結(jié)果比較顯示,支持向量回歸模型有最好的逼近性質(zhì),考慮電力系統(tǒng)負(fù)荷、空氣溫度和自然光照因素。課題的理論意義在于開發(fā)一種應(yīng)用粒子群優(yōu)化算法優(yōu)化支持向量機(jī)的參數(shù)的預(yù)測模型。該方法建立了電力消費(fèi)、空氣溫度和自然光照之間非線性關(guān)系以提高模型的預(yù)測精度。實(shí)際意義在于開發(fā)的模型可用來預(yù)測區(qū)域調(diào)度辦事處分支機(jī)構(gòu)的能源消費(fèi),批發(fā)發(fā)電公司和領(lǐng)土產(chǎn)生的公司、區(qū)域網(wǎng)公司,能源銷售公司,以及在分派辦事處的個(gè)別公司成員的批發(fā)或零售電力市場和權(quán)力。短期預(yù)測的計(jì)算機(jī)程序也被在MatLab環(huán)境下開發(fā)應(yīng)用開發(fā)。
【文章來源】：蘭州交通大學(xué)甘肅省
【文章頁數(shù)】：67 頁
【學(xué)位級別】：碩士
【文章目錄】：
Abstract
摘要
1. Review and analysis of modern methods and mathematical models to predict electricity consumption
1.1 Classification of short-term load forecasting methods
1.2 Statistical methods of forecasting
1.2.1 Methods for regression
1.2.2 Time series methods
1.2.3 Methods based on wavelet transform of time series
1.3 Methods of artificial intelligence
1.3.1 Methods based on neural network models
1.3.2 Methods based on fuzzy logic
1.3.3 Support vector method
1.4 Evolutionary algorithms
1.5 Requirements for short-term forecasting of electricity consumption
1.6 Main problems of short-term forecasting of electricity consumption
1.6.1 Accuracy of the input - output relationship hypothesis
1.6.2 Prediction of abnormal days
1.6.3 Inaccurate weather forecast data
1.7 Review of current literature on the problem of short-term power consumption forecasting
1.7.1 Models of neural networks
1.7.2 Models of neuro-fuzzy networks
1.7.3 Model of wavelet transform
1.7.4 Regression models
1.8 Conclusions
2. Time series analysis of electricity consumption and its determinants
2.1 Characteristics of the electrical load diagrams of the power system
2.2 Time series of power consumption and influencing factors
2.3 Seasonal and meteorological factors affecting power consumption
2.4 Temperature and light: the analysis of their impact on power consumption in the control room operating area
2.5 Random disturbances
2.6 Conclusions
3. Modelling short term future energy consumption based on neural networks and evolutionary algorithms
3.1 Short-term load forecasting using artificial neural network
3.2 Short-term load forecasting using artificial neural networks and particle swarm optimization algorithm
3.3 Short-term load forecasting using artificial neural networks and particle swarm optimization algorithm
3.3.1 Data analysis and pre-processing
3.3.2 The number of layers, neurons and transfer functions
3.3.3 Training of built neural networks
3.3.4 Architecture of the ANN for the operating zone
3.3.5 The choice of input variables
3.3.6 Building the structure of neural network
3.3.7 Selection of data for training, testing and validation
3.3.8 Simulation results
3.4 Training the ANN on the basis of self-organization
3.4.1 Dataset for the study
3.4.2 Training of self-organizing maps
3.4.3 The results of clustering and prediction
3.4.4 Performance criteria
3.4.5 Simulation results
3.5 Conclusions
4. Models of future energy consumption based on neural fuzzy network and support vector method
4.1 Predicting power consumption using adaptive neural fuzzy network
4.1.1 The architecture of neuro-fuzzy model
4.1.2 Hybrid algorithm for training neural networks
4.1.3 Simulation result
4.2 Energy consumption forecasting using support vector
4.2.1 Simulation results
4.3 Forecasting of power consumption based on the support vector method and particle swarm algorithm
4.3.1 Load forecasting steps and processes
4.3.2 A set of analysis parameters
4.3.3 Simulation results
4.4 Conclusions
Summarize
Acknowledgement
References
Research achievement during working for the degree
【參考文獻(xiàn)】：
期刊論文
[1]基于神經(jīng)網(wǎng)絡(luò)和模糊理論的短期負(fù)荷預(yù)測[J]. 趙宇紅,唐耀庚,張韻輝. 高電壓技術(shù). 2006(05)
本文編號：3266457
【文章來源】：蘭州交通大學(xué)甘肅省
【文章頁數(shù)】：67 頁
【學(xué)位級別】：碩士
【文章目錄】：
Abstract
摘要
1. Review and analysis of modern methods and mathematical models to predict electricity consumption
1.1 Classification of short-term load forecasting methods
1.2 Statistical methods of forecasting
1.2.1 Methods for regression
1.2.2 Time series methods
1.2.3 Methods based on wavelet transform of time series
1.3 Methods of artificial intelligence
1.3.1 Methods based on neural network models
1.3.2 Methods based on fuzzy logic
1.3.3 Support vector method
1.4 Evolutionary algorithms
1.5 Requirements for short-term forecasting of electricity consumption
1.6 Main problems of short-term forecasting of electricity consumption
1.6.1 Accuracy of the input - output relationship hypothesis
1.6.2 Prediction of abnormal days
1.6.3 Inaccurate weather forecast data
1.7 Review of current literature on the problem of short-term power consumption forecasting
1.7.1 Models of neural networks
1.7.2 Models of neuro-fuzzy networks
1.7.3 Model of wavelet transform
1.7.4 Regression models
1.8 Conclusions
2. Time series analysis of electricity consumption and its determinants
2.1 Characteristics of the electrical load diagrams of the power system
2.2 Time series of power consumption and influencing factors
2.3 Seasonal and meteorological factors affecting power consumption
2.4 Temperature and light: the analysis of their impact on power consumption in the control room operating area
2.5 Random disturbances
2.6 Conclusions
3. Modelling short term future energy consumption based on neural networks and evolutionary algorithms
3.1 Short-term load forecasting using artificial neural network
3.2 Short-term load forecasting using artificial neural networks and particle swarm optimization algorithm
3.3 Short-term load forecasting using artificial neural networks and particle swarm optimization algorithm
3.3.1 Data analysis and pre-processing
3.3.2 The number of layers, neurons and transfer functions
3.3.3 Training of built neural networks
3.3.4 Architecture of the ANN for the operating zone
3.3.5 The choice of input variables
3.3.6 Building the structure of neural network
3.3.7 Selection of data for training, testing and validation
3.3.8 Simulation results
3.4 Training the ANN on the basis of self-organization
3.4.1 Dataset for the study
3.4.2 Training of self-organizing maps
3.4.3 The results of clustering and prediction
3.4.4 Performance criteria
3.4.5 Simulation results
3.5 Conclusions
4. Models of future energy consumption based on neural fuzzy network and support vector method
4.1 Predicting power consumption using adaptive neural fuzzy network
4.1.1 The architecture of neuro-fuzzy model
4.1.2 Hybrid algorithm for training neural networks
4.1.3 Simulation result
4.2 Energy consumption forecasting using support vector
4.2.1 Simulation results
4.3 Forecasting of power consumption based on the support vector method and particle swarm algorithm
4.3.1 Load forecasting steps and processes
4.3.2 A set of analysis parameters
4.3.3 Simulation results
4.4 Conclusions
Summarize
Acknowledgement
References
Research achievement during working for the degree
【參考文獻(xiàn)】：
期刊論文
[1]基于神經(jīng)網(wǎng)絡(luò)和模糊理論的短期負(fù)荷預(yù)測[J]. 趙宇紅,唐耀庚,張韻輝. 高電壓技術(shù). 2006(05)
本文編號：3266457
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