Forecasting next-day electricity demand and prices based on functional models
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Forecasting next-day electricity demand and prices based on functional models Francesco Lisi1,2 · Ismail Shah3 Received: 2 April 2017 / Accepted: 22 August 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Efficient modeling and forecasting of electricity demand and prices is an important issue in competitive electricity markets. This work investigates the forecasting performance of several models for the 1-day-ahead prediction of demand and prices on four electricity markets (APX Power-UK, Nord Pool, PJM and IPEX). All the models are based on two steps: a nonparametric estimation of some deterministic components, followed by the choice of a suitable model for the residual stochastic component. This latter step includes univariate and multivariate as well as parametric and nonparametric models, with particular emphasis on the functional approach, that models the whole daily profile as a single functional observation. More specifically, the models involved are: a linear model and a nonlinear (nonparametric) autoregressive model, a vector autoregressive model and four autoregressive functional specifications. Prediction covers a whole year. Comparisons are based both on descriptive statistics and on statistical tests of equal forecasting accuracy. Though results partly depend on specific markets, a double functional model always proved to be the best- or no different from the best-model, highlighting the effectiveness of the functional approach. Keywords Electricity demand forecasting · Electricity prices forecasting · Functional models · Nonparametric models · APX, NP, IPEX, PJM
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Ismail Shah [email protected] Francesco Lisi [email protected]
1
Department of Statistical Sciences, University of Padova, Via C. Battisti, 241, 35121 Padova, Italy
2
Interdepartmental Centre for Energy Economics and Technology “Giorgio Levi Cases”, University of Padua, Padua, Italy
3
Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
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F. Lisi, I. Shah
1 Introduction In competitive electricity markets, accurate modeling and forecasting of electricity demand and prices are crucial for effective planning and operation of power systems. Since electricity cannot be stored in large quantities and has to be delivered immediately to end user, over- or under-estimating the demand involves several problems for producers, energy suppliers, system operators and other market participants. For example, over-estimation can lead to excessive energy purchasing or unnecessary production, resulting in substantial financial losses. Likewise, under-estimation makes it necessary to balance the system, whatever the cost, and this can generate financial distress. Electricity prices are also highly volatile and this gives rise to significant price risk for market participants. This is the main reason why, in the last two decades, increasing attention has been paid to efficient forecasting of electricity loads and prices ([9,48,76], among others). Market participants may be interested in diffe
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