Multiple seasonal cycles forecasting model: the Italian electricity demand.

*(English)*Zbl 1416.62652Summary: Forecasting energy load demand data based on high frequency time series has become of primary importance for energy suppliers in nowadays competitive electricity markets. In this work, we model the time series of Italian electricity consumption from 2004 to 2014 using an exponential smoothing approach. Data are observed hourly showing strong seasonal patterns at different frequencies as well as some calendar effects. We combine a parsimonious model representation of the intraday and intraweek cycles with an additional seasonal term that captures the monthly variability of the series. Irregular days, such as public holidays, are modelled separately by adding a specific exponential smoothing seasonal term. An additive ARMA error term is then introduced to lower the volatility of the estimated trend component and the residuals’ autocorrelation. The forecasting exercise demonstrates that the proposed model performs remarkably well, in terms of lower root mean squared error and mean absolute percentage error criteria, in both short term and medium term forecasting horizons.

##### MSC:

62P20 | Applications of statistics to economics |

62M20 | Inference from stochastic processes and prediction |

91B74 | Economic models of real-world systems (e.g., electricity markets, etc.) |

##### Keywords:

electricity demand forecasting; exponential smoothing; multiple seasonality; single source of error models##### Software:

expsmooth
PDF
BibTeX
XML
Cite

\textit{M. Bernardi} and \textit{L. Petrella}, Stat. Methods Appl. 24, No. 4, 671--695 (2015; Zbl 1416.62652)

Full Text:
DOI

##### References:

[1] | Bartolomei, SM; Sweet, AL, A note on a comparison of exponential smoothing methods for forecasting seasonal series, Int J Forecast, 5, 111-116, (1989) |

[2] | Billah, B; King, ML; Snyder, RD; Koehler, AB, Exponential smoothing model selection for forecasting, Int J Forecast, 22, 239-247, (2006) |

[3] | Cancelo, JR; Espasa, A; Grafe, R, Forecasting from one day to one week ahead for the Spanish system operator, Int J Forecast, 24, 588-602, (2008) |

[4] | Livera, AM; Hyndman, RJ; Snyder, RD, Forecasting time series with complex seasonal patterns using exponential smoothing, J Am Stat Assoc, 106, 1513-1527, (2011) · Zbl 1234.62123 |

[5] | Durbin J, Koopman SJ (2012) Time series analysis by state space methods, 2nd edn. Oxford University Press, Oxford · Zbl 1270.62120 |

[6] | Gardner, ES, Exponential smoothing: the sate of the art-part II, Int J Forecast, 22, 637-666, (2006) |

[7] | Gould, PG; Koehler, AB; Ord, JK; Snyder, RD; Hyndman, RJ; Vahid-Araghi, F, Forecasting time series with multiple seasonal patterns, Eur J Oper Res, 191, 207-222, (2008) · Zbl 1142.62070 |

[8] | Hagan, MT; Behr, SM; Ord, JK, The time series approach to short-term forecasting, IEEE Trans Power Syst, 2, 785-791, (1987) |

[9] | Harvey AC (1989) Forecasting structural time series models and the Kalman filter. Cambridge University Press, Cambridge |

[10] | Harvey, AC; Koopman, SJ; Riani, M, Forecasting hourly electricity demand using time-varying splines, J Am Stat Assoc, 88, 1228-1236, (1997) |

[11] | Harvey, AC; Koopman, SJ; Riani, M, The modelling and seasonal adjustment of weekly observations, J Bus Econ Stat, 15, 354-368, (1997) |

[12] | Hippert, HS; Pedreira, CE; Souza, RC, Neural networks for short-term load forecasting: a review and evaluation, IEEE Trans Power Syst, 16, 44-55, (2001) |

[13] | Holt CC (1957) Forecasting trends and seasonals by exponentially weighted averages. Carnegie Institute of Technology, Pittsburgh ONR memorandum no 52 |

[14] | Hyndman, RJ; Koehler, AB; Snyder, RD; Grose, S, A state space framework for automatic forecasting using exponential smoothing methods, Int J Forecast, 18, 439-454, (2002) |

[15] | Hyndman, RJ; Koehler, AB; Ord, JK; Snyder, RD, Prediction intervals for exponential smoothing using two new classes of state space models, J Forecast, 24, 17-37, (2005) |

[16] | Hyndman RJ, Koehler AB, Ord JK, Snyder RD (2008b) Forecasting with exponential smoothing. The state space approach. Springer, Berlin · Zbl 1211.62165 |

[17] | Hu Z, Bao Y, Xiong T (2013) Electricity load forecasting using support vector regression with memetic algorithms. Sci World J Article ID 292575, p 10 doi:10.1155/2013/292575 |

[18] | Huang, SJ; Shih, KR, Short-term load forecasting via ARMA model identification including Nongaussian process considerations, IEEE Trans Power Syst, 18, 673-679, (2003) |

[19] | Kuusisto, S; Lehtokangas, M; Saarinen, J; Kaski, K, Short term electric load forecasting using a neural network with fuzzy hidden neurons, Neural Comput Appl, 6, 42-56, (1997) |

[20] | Lu, CN; Vemuri, S, Neural network based short term load forecasting, IEEE Trans Power Syst, 8, 336-342, (1993) |

[21] | Mandal, P; Senjyu, T; Urasaki, N; Funabashi, T, A neural network based several-hour-ahead electric load forecasting using similar days approach, Int J Electr Power Energy Syst, 28, 367-373, (2006) |

[22] | Mbmalu, GAN; El-Hawary, ME, Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation, IEEE Trans Power Syst, 8, 343-348, (1993) |

[23] | Makridakis, S; Hibon, M, The M3-competition: results, conclusions and implications, Int J Forecast, 16, 451-476, (2000) |

[24] | Moghram, I; Rahman, S, Analysis and evaluation of five short-term load forecasting techniques, IEEE Trans Power Syst, 4, 1484-1491, (1989) |

[25] | Monahan, JF, A note on enforcing stationarity in autoregressive moving average models, Biometrika, 71, 403-404, (1984) |

[26] | Ord, JK; Koehler, AB; Snyder, RD, Estimation and prediction for a class of dynamic nonlinear statistical models, J Am Stat Assoc, 92, 1621-1629, (1997) · Zbl 0912.62104 |

[27] | Papalexopoulos, AD; Hesterberg, TC, A regression-based approach to short-term load forecasting, IEEE Trans Power Syst, 5, 1535-1550, (1990) |

[28] | Rahman, S; Hazim, O, Load forecasting for multiple sites: development of an expert system-based technique, Electr Power Syst Res, 39, 161-169, (1996) |

[29] | Snyder, RD, Recursive estimation of dynamic linear models, J R Stat Soc Ser B, 47, 272-276, (1985) |

[30] | Taylor, JW, Short-term electricity demand forecasting using double seasonal exponential smoothing, J Oper Res Soc, 54, 799-805, (2003) · Zbl 1097.91541 |

[31] | Taylor, JW, A comparison of time series methods for forecasting intraday arrivals at a call center, Manag Sci, 54, 253-265, (2008) · Zbl 1232.90214 |

[32] | Taylor, JW, Triple seasonal methods for short-term electricity demand forecasting, Eur J Oper Res, 204, 139-152, (2010) · Zbl 1178.91165 |

[33] | Taylor, JW, Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles, Int J Forecast, 26, 627-646, (2010) |

[34] | Taylor, JW; Snyder, RD, Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing, Omega, 40, 748-757, (2012) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.