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Forecasting next-day electricity prices by time series models

Forecasting next-day electricity prices by time series models,10.1109/TPWRS.2002.1007902,IEEE Transactions on Power Systems,Francisco J. Nogales,Javie

Forecasting next-day electricity prices by time series models   (Citations: 224)
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In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented
Journal: IEEE Transactions on Power Systems - IEEE TRANS POWER SYST , vol. 17, no. 2, pp. 342-348, 2002
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    • ...In most competitive electricity markets, price series present the following features: high frequency, nonconstant mean and variance, daily and weekly seasonality, calendar effect on weekend and public holidays, high volatility, and high percentage of unusual prices [2]...

    J. P. S. Catalaoet al. Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Fo...

    • ...Consequently, numerous data-driven approaches have been proposed for modeling and forecasting short-term electricity market prices [2]‐[16]...
    • ...First is the problem of outliers, where pricesdonotfollowtheobservedhistoricalpatterns[2].Outliers or abnormal prices generally result from supply scarcity or unexpected operational events such as the forced outage of a generation unit...
    • ...Second, electricity prices are not stationary and show strong daily and weekly seasonalities [2], [5]...
    • ...In order to achieve better stationarity in the data, several data transformation approaches such as differencing, Box-Cox, and wavelet transformations have been utilized [2], [7], [24]...
    • ...Linear [2] and nonlinear [4] autocorrelation studies have shown that electricity market price time series are strongly autocorrelated...
    • ...In other words, has strong correlation with time lagged prices, for instance, . Therefore, time lagged prices have been consistently included in the models proposed in the literature for numerical price forecasting [2], [4], [9]...

    Hamidreza Zareipouret al. Classification of Future Electricity Market Prices

    • ...Although the use of exogenous variables other than MCPs (such as electricity load, temperatures, etc.) have been proposed in previous studies, it has been inconclusive as to which variables, if any, contribute to increased explanatory power of a model [10, 17, 31]...

    Derrick Takeshi Mirikitaniet al. Nonlinear maximum likelihood estimation of electricity spot prices usi...

    • ...In other words, these methods try to approximately model the underlying process of price formation in a market and use the model to forecast the exact value of future prices [7]‐[10]...
    • ...Linear [7] and nonlinear [22] correlation studies have shown that there is a strong correlation between price at any given hour and a number of preceding prices...
    • ...In other words, p t+l, l ∈{ 1, 2, ..., 24} has a strong correlation with pt+l−q, where typical values of q are as follows [7], [9], [22]:...
    • ...Thus, these preceding prices, along with other features (e.g., load), have been consistently included in the price models proposed in the literature for numerical electricity market price forecasting [7], [9], [22]...

    Hamidreza Zareipouret al. Electricity price thresholding and classification

    • ...For this system, it has been considered that DistCo at bus 5 is willing to pay 25 $/MWh (at maximum) for its price responsive or PRDS demand consumption, whereas LMP at bus 5 can go up to 28.0 $/MWh under price taking bid, which can be forecasted from near history of day-ahead schedule [26]‐[28]...

    Kanwardeep Singhet al. Influence of Price Responsive Demand Shifting Bidding on Congestion an...

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