Forecasting the Colombian Electricity Spot Price Under a Functional Approach (original) (raw)
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International Journal of Electrical Power & Energy Systems, 2012
One-day-ahead forecasting of electricity demand and price is an important issue in competitive electric power markets. These problems have been studied in previous works using, for instance, ARIMA models, dynamic regression and neural networks. This paper provides two new methods to address these two prediction setups. They are based on using nonparametric regression techniques with functional explanatory data and a semifunctional partial lineal model. Results of these methods for the electricity market of mainland Spain, in years 2008Spain, in years -2009 reported. The new forecasting functional methods are compared with a naïve method and with seasonal ARIMA forecasts.
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Mathematical Problems in Engineering
In today’s liberalized electricity markets, modeling and forecasting electricity demand data are highly important for the effective management of the power system. However, electricity demand forecasting is a challenging task due to the specific features it exhibits. These features include the presence of extreme values, spikes or jumps, multiple periodicities, long trend, and bank holiday effect. In addition, the forecasts are required for a complete day as electricity demand is decided a day before the physical delivery. Therefore, this study aimed to investigate the forecasting performance of models based on functional data analysis, a relatively less explored area in energy research. To this end, the demand time series is first treated for the extreme values. The filtered series is then divided into deterministic and stochastic components. The generalized additive modeling technique is used to model the deterministic component, whereas functional autoregressive (FAR), FAR with e...
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International Journal of Electrical Power & Energy Systems, 2018
This paper provides two procedures to obtain prediction intervals for electricity demand and price based on functional data. The proposed procedures are related to one day ahead pointwise forecast. In particular, the first method uses a nonparametric autoregressive model and the second one uses a partial linear semi-parametric model, in which exogenous scalar covariates are incorporated in a linear way. In both cases, the proposed procedures for the construction of the prediction intervals use residual-based bootstrap algorithms, which allows also to obtain estimates of the prediction density. Applications to the Spanish Electricity Market, in year 2012, are reported. This work extends and complements the results of Aneiros et al. (2016), focused on pointwise forecasts of next-day electricity demand and price daily curves.
IEEE Access
Over the last three decades, accurate modeling and forecasting of electricity prices has become a key issue in competitive electricity markets. As electricity price series usually exhibit several complex features, such as high volatility, seasonality, calendar effect, non-stationarity, non-linearity and mean reversion, price forecasting is not a trivial task. However, participants of electricity market need price forecast to make decisions in their daily activity in the market, such as trading, risk management or future planning. In this study we consider linear and nonlinear models for one-day-ahead forecast of electricity prices using components estimation techniques. This approach requires to filter out the structural, deterministic components from the original time series and to model the residual component by means of some stochastic process. The final forecast is obtained by combining the predictions of both these components. In this work, linear and non-linear models are applied to both, deterministic and stochastic, components. In the case of stochastic component, AutoRegressive, Nonparametric AutoRegressive, Functional AutoRegressive, and Nonparametric Functional AutoRegressive have been considered. Furthermore, two naïve benchmarks are applied directly to the price time series and their results are compared with our proposed models. An application of the proposed methodology is presented for the Italian electricity market (IPEX). Our analysis suggests that, in terms of Mean Absolute Error, Mean Absolute Percentage Error, and Pearson correlation coefficient, best results are obtained when deterministic component is estimated by using parametric approach. Further, Functional AutoRegressive model performs relatively better than the rest while Nonparametric AutoRegressive is highly competitive. INDEX TERMS Electricity prices forecasting, parametric and nonparametric models, univariate and multivariate time series, modeling and forecasting, IPEX.
Time series of functional data
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We develop time series analysis of functional data observed discretely, treating the whole curve as a random realization from a distribution on functions that evolve over time. The method consists of principal components analysis of functional data and subsequently modeling the principal component scores as vector ARMA process. We carry out the estimation of VARMA parameters using the equivalent state space representation. We derive asymptotic properties of the estimators and the fits. We apply the method to two different data sets. For term structures of interest rates, this provides a unified framework for studying the time and maturity components of interest rates under one set-up with few parametric assumptions. We compare our forecasts to the parametric model. Secondly, we apply this approach to hourly spot prices of electricity and obtain fits and forecasts that are better than those existing in the electricity literature.
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Forecasting Time-Varying Covariance Matrices in Intradaily Electricity Spot Prices
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This paper deals with analysing and forecasting intradaily volatility in electricity spot prices. We analyse the hourly spot prices from the Argentine Electricity Market by grouping prices in three daily series (block bids). We estimate the VAR model for the conditional mean structure and several multivariate analysis based on the multivariate GARCH models, specifically the orthogonal GARCH by Alexander (2000) and the constrained multivariate GARCH by Engle and Mezrich (1996). We also measure the forecasting performance of the daily block bid volatilities and covariances under both approaches obtaining similar results. This methodology could be used for managing risk of block bid portfolios and also for the valuation of derivatives on intradaily time-blocks of electricity spot prices.
Short-term forecasting of electricity prices in the Colombian electricity market
Iet Generation Transmission & Distribution, 2009
The restructuring of the electricity-generating industry from protected monopoly to an open competitive market has presented producers with a problem scheduling generation: finding the optimal bidding strategy to maximise their profits. In order to solve this scheduling problem, a reliable system capable of forecasting electricity prices is needed. This work evaluates the forecasting capabilities of several modelling techniques for the next-day-prices forecasting problem in the Colombian market, measured in USD/MWh. The models include exogenous variables such as reservoir levels and load demand. Results show that a segmentation of the prices into three intervals, based on load demand behaviour, contribute to an important standard deviation reduction. Regarding the models under analysis, Takagi-Sugeno-Kang models and ARMAX models identified by means of a Kalman filter perform the best forecasting, with an error rate below 6%.
On the Use of Functional Additive Models for Electricity Demand and Price Prediction
IEEE Access, 2018
This paper presents an application of functional additive models in the context of electricity demand and price prediction. Data from the Spanish electricity market are used to obtain the pointwise predictions. Also prediction intervals, based on a bootstrap procedure, are computed. This approach is compared with the use of other functional regression methods applied to the same data set by Aneiros et al. (2016). INDEX TERMS Additive model, functional data, functional time series forecasting, load and price, prediction intervals.