Prediction of Time Series Analysis of Power Usage Based on R

发布时间：2024-05-10 22:39

　　在竞争激烈的零售市场中,电源商正在寻求通过智能电表分析客户的每一个用电数据,这将为他们提供大量机会,以便在非常大量的智能电网中实现对客户电力消耗需求的额外了解。通常,有大量的分析解决方案来呈现家庭的设施使用情况,但这些类型的解决方案并未提供准确的信息。因此,我们尝试对个体家庭能源消费模式进行全面分析,并设计一个家庭层面预测模型,利用历史能耗数据预测未来有价值的实际需求和相应的有关需求。本文提出了一种完全独特的方法来预测配电系统中的输电时间序列分析,该方法显示了不同消费行为的比例,以及相邻时段内不同时段的消费水平。所提出的方法预测了客户使用智能电表管理其家庭电力数据收集的合法性,并帮助客户系统操作员检测和控制负载需求。该模型在大型数据集中查找不同时期的各种功率趋势。通过使用时间序列数据方法和预测模型的预测来进行评估。结果表明,具有良好准确性的预测可以帮助公司和最终用户通过将功耗从高峰时段转移到非高峰时段来控制其负载需求。应用知识发现回归模型,在一周内明显改善了电力趋势消费,并帮助用户改善客户需求,如节能,低价和管理。

【文章页数】：72 页

【学位级别】：硕士

【文章目录】：
Abstract
摘要
Abbreviations
Chapter1 General Introduction
    1.1 Purpose and significance of research
    1.2 Related Technology
        1.2.1 Big data
        1.2.2 Analytics
        1.2.3 R and RStudio tools for programming
    1.3 Foundation of research and development
    1.4 Research Methods
    1.5 Work summary
Chapter2 Preliminaries
    2.1 Introduction
    2.2 Data Mining Theory
        2.2.1 Introduction
        2.2.2 Types of data mining
        2.2.3 Data mining process
    2.3 Time Series Analysis
        2.3.1 Time series classification
        2.3.2 Time series aim
        2.3.3 Times series components
        2.3.4 Time series forecasting
            2.3.4.1 Forecasting without external factors
            2.3.4.2 Forecasting with external factors
        2.3.5 Forecasting accuracy
        2.3.6 Data preprocessing
            2.3.6.1 Outliers detection
            2.3.6.2 Denoising and Smoothing
            2.3.6.3 Differencing
            2.3.6.4 Data scaling
            2.3.6.5 Normalization
    2.4 Forecasting Methods
        2.4.1 Regression models
        2.4.2 Autoregressive and moving average models
        2.4.3 Exponential smoothing models
        2.4.4 Artificial neural networks models
        2.4.5 Markov chain model
    2.5 Load forecast household electricity
Chapter3 Design of Analysis System
    3.1 Introduction
    3.2 Data collection
        3.2.1 Description of dataset
        3.2.2 Electrical smart meters
        3.2.3 Measurement description
        3.2.4 Data set attribute information
    3.3 Data preparation and preprocessing
    3.4 Feature selection and modeling
        3.4.1 Data visualization and transformation
        3.4.2 The annual household electricity consumption
    3.5 Time series data
        3.5.1 Time series concepts
        3.5.2 Decomposition of time series
            3.5.2.1 Change the data format
            3.5.2.2 Analytics data exploration
    3.6 Construction and time series forecasting model
        3.6.1 Automated model time series forecasting ETS(A,N,A)
        3.6.2 Forecasting method
            3.6.2.1 Exponential smoothing
            3.6.2.2 Simple exponential smoothing(SES)
            3.6.2.3 Forecasting for further more times points smoothing
        3.6.3 ARIMA models
        3.6.4 Advanced forecasting methods
        3.6.5 Prediction model evaluation
    3.7 Monthly trend and forecasting results
    3.8 Knowledge discovery regression model
        3.8.1 Case study
        3.8.2 Plotting power consumption
        3.8.3 Implementation over one week
        3.8.4 Determine the trend of weekly consumed energy
Chapter4 Discussion and Conclusion
    4.1 Discussion
    4.2 Conclusion
Reference
Acknowledgement
Appendices



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