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Deterministic Prediction of Electric Load Time Series

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Non-intrusive Load Monitoring

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Abstract

This chapter first gives an introduction of the current researches of deterministic prediction of electric load time series, and then the application of the Autoregressive Integrated Moving Average (ARIMA) based models and the Elman neural network based models are investigated. Several experiments are carried out to give comprehensive evaluations of different forecasting models.

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References

  • Amin SB, Alam T (2018) The relationship between energy consumption and sectoral output in Bangladesh: An empirical analysis. J Dev Areas 52(3):39–54

    Article  Google Scholar 

  • Chatfield C, Weigend AS (1994) Time series prediction: forecasting the future and understanding the past. In: Weigend AS, Gershenfeld NA (eds) Future of time series. Addison-Wesley, Reading, MA, pp 1–70. Int J Forecast 10(1):161–163

    Google Scholar 

  • Chen K, Chen K, Qin W, He Z, He J (2018) Short-term load forecasting with deep residual networks. IEEE Trans Smart Grid (99):1–1

    Google Scholar 

  • Cooper DE (2012) Intelligent transportation systems for smart cities: a progress review. Sci China (Inf Sci) 55(12):2908–2914

    Google Scholar 

  • Diks C, Houwelingen JCV, Takens F, Degoede J (1995) Reversibility as a criterion for discriminating time series. Phys Lett A 201(2–3):221–228

    Article  Google Scholar 

  • Ehrenfeld D (2010) Sustainability: living with the imperfections. Conserv Biol 19(1):33–35

    Article  Google Scholar 

  • Feinberg EA, Genethliou D (2005) Load forecasting. In: Applied mathematics for restructured electric power systems. Springer

    Google Scholar 

  • Ge X, Yang J, Gharavi H, Sun Y (2017) Energy efficiency challenges of 5G small cell networks. IEEE Commun Mag 55(5):184–191

    Article  Google Scholar 

  • Huang NE, Zheng S, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Chi CT, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Math Phys Eng Sci 454(1971):903–995

    Article  MathSciNet  Google Scholar 

  • Inoue A (2008) AR and MA representation of partial autocorrelation functions, with applications. Probab Theory Relat Fields 140(3–4):523–551

    MathSciNet  MATH  Google Scholar 

  • Kang C, Xia Q (2004) Review of power system load forecasting and its development. Autom Electr Power Syst 28(17):1–11

    Google Scholar 

  • Köker R (2005) Reliability-based approach to the inverse kinematics solution of robots using Elman’s networks. Eng Appl Artif Intell 18(6):685–693

    Article  Google Scholar 

  • Madsen H (2007) Time series analysis. Chapman & Hall/CRC Boca Raton Fl 27 (1864):103–121

    Google Scholar 

  • Markovié ML, Fraissler WF (2010) Short-term load forecast by plausibility checking of announced demand: an expert-system approach. Int Trans Electr Energy Syst 3(5):353–358

    Google Scholar 

  • Mengelkamp E, Notheisen B, Beer C, Dauer D, Weinhardt C (2018) A blockchain-based smart grid: towards sustainable local energy markets. Comput Sci Res Dev 33(1–2):207–214

    Article  Google Scholar 

  • Migendt M (2017) public policy influence on renewable energy investments—a panel data study across OECD countries. In: ULB institutional repository. Springer Gabler, Wiesbaden

    Chapter  Google Scholar 

  • Nalcaci G, Özmen A, Weber GW (2018) Long-term load forecasting: models based on MARS, ANN and LR methods. CEJOR 2:1–17

    MATH  Google Scholar 

  • Shi H, Xu M, Ran L (2018) Deep learning for household load forecasting—a novel pooling deep RNN. IEEE Trans Smart Grid 9(5):5271–5280

    Article  Google Scholar 

  • Tong W, Zhang M, Yu Q, Zhang H (2012) Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. J Appl Geophys 83(29–34):29–34

    Google Scholar 

  • Watanabe S (2013) A widely applicable Bayesian information criterion. J Mach Learn Res 14(1):867–897

    MathSciNet  MATH  Google Scholar 

  • Xiao L, Wei S, Yu M, Jing M, Jin C (2017) Research and application of a combined model based on multi-objective optimization for electrical load forecasting. Energy 119:1057–1074

    Article  Google Scholar 

  • Yiyan LI, Han D, Yan Z (2017) Long-term system load forecasting based on data-driven linear clustering method. J Modern Power Syst Clean Energy 17:1–11

    Google Scholar 

  • Zeng B, Zhang J, Yang X, Wang J, Dong J, Zhang Y (2014) Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response. IEEE Trans Power Syst 29(3):1153–1165

    Article  Google Scholar 

  • Zhang D, Miao X, Liu L, Zhang Y, Liu K (2015) Research on development strategy for smart grid big data. Proc Csee 35(1):2–12

    Google Scholar 

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Authors and Affiliations

  1. School of Traffic and Transportation Engineering, Central South University, Changsha, China

    Hui Liu

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  1. Hui Liu
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Correspondence to Hui Liu .

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Liu, H. (2020). Deterministic Prediction of Electric Load Time Series. In: Non-intrusive Load Monitoring. Springer, Singapore. https://doi.org/10.1007/978-981-15-1860-7_9

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  • DOI: https://doi.org/10.1007/978-981-15-1860-7_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1859-1

  • Online ISBN: 978-981-15-1860-7

  • eBook Packages: EnergyEnergy (R0)

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