Academia.eduAcademia.edu

Figure 4 – uploaded by rosa espinola

See full PDFdownloadDownload figure
Taking this into account, a periodic dynamic regression model whose parameters switch according to the

Figure 4 Taking this into account, a periodic dynamic regression model whose parameters switch according to the

Related Figures (14)

Fig. 1. Spanish day-ahead electricity market prices, and load and wind generation forecasts made by the system operator. prices, as weighted nearest neighbors in (Troncoso, Riquelme et al. 2007). cope with this task. For instance, an Elman recurrent neural network is used in (Andalib, Safavieh et al
Fig. 1. Spanish day-ahead electricity market prices, and load and wind generation forecasts made by the system operator. prices, as weighted nearest neighbors in (Troncoso, Riquelme et al. 2007). cope with this task. For instance, an Elman recurrent neural network is used in (Andalib, Safavieh et al
Fig. 2. Daily (solid line) and weekly (dash line) components of the holt-winters decomposition. B. Exponential smoothing with double seasonality A description of the identification of exponential smoothing models for double seasonality can be found
Fig. 2. Daily (solid line) and weekly (dash line) components of the holt-winters decomposition. B. Exponential smoothing with double seasonality A description of the identification of exponential smoothing models for double seasonality can be found
DOUBLE SEASONAL EXPONENTIAL SMOOTHING MODELS FITTED Fig. 2 shows the weekly and daily seasonal components for two weeks within the fitting period. The daily
DOUBLE SEASONAL EXPONENTIAL SMOOTHING MODELS FITTED Fig. 2 shows the weekly and daily seasonal components for two weeks within the fitting period. The daily
Towever, the MAPE corresponding to the 24-step-ahead forecast of the out-of-sample period hardly outperformed the ARIMA model and it turned out equal than the exponential smoothing's one: 8.53%. The cross correlation function (CCF) of the differenced and log-transformed wind power production
Towever, the MAPE corresponding to the 24-step-ahead forecast of the out-of-sample period hardly outperformed the ARIMA model and it turned out equal than the exponential smoothing's one: 8.53%. The cross correlation function (CCF) of the differenced and log-transformed wind power production
MEAN OF SQUARE ERRORS OF THE DY NAMIC REGRESSION MODEL BY DAY OF THE WEEK TABLE II
MEAN OF SQUARE ERRORS OF THE DY NAMIC REGRESSION MODEL BY DAY OF THE WEEK TABLE II
Fig. 4. Bidding curves for 18 and 21 March 2003 (Sunday and Wednesday, respectively). Note the intersection point of supply and demand curves. switching model present two fixed unit roots: one regular and another corresponding to the within-week
Fig. 4. Bidding curves for 18 and 21 March 2003 (Sunday and Wednesday, respectively). Note the intersection point of supply and demand curves. switching model present two fixed unit roots: one regular and another corresponding to the within-week
SUMMARY OF THE FITTED PERIODIC MODELS more significant increase in price for the same increase in the effective demand. is displaced to the left in the figure, and enters a segment of higher slope. A higher slope is equivalent to a
SUMMARY OF THE FITTED PERIODIC MODELS more significant increase in price for the same increase in the effective demand. is displaced to the left in the figure, and enters a segment of higher slope. A higher slope is equivalent to a
Fig. 5. Price (solid line) and demand (dash line, in different scales) in some days of August. Note the spikes in the price when demand is high, and saturations when demand is low. of demand hour. Model 5 uses 12 periods, as a combination of model 1 with model 4. None of these models
Fig. 5. Price (solid line) and demand (dash line, in different scales) in some days of August. Note the spikes in the price when demand is high, and saturations when demand is low. of demand hour. Model 5 uses 12 periods, as a combination of model 1 with model 4. None of these models
Fig. 6. Analysis of the input/output sensitivities for the neural network. ‘ig.6. The upper picture illustrates the 95%-percentiles of the sensitivity absolute values normalize and fj are all hyperbolic tangent functions. The analysis of the input/output sensitivities (Oy/Ox) is shown in
Fig. 6. Analysis of the input/output sensitivities for the neural network. ‘ig.6. The upper picture illustrates the 95%-percentiles of the sensitivity absolute values normalize and fj are all hyperbolic tangent functions. The analysis of the input/output sensitivities (Oy/Ox) is shown in
Regarding the univariate tested methods, exponential smoothing performed slightly better than ARIMA,
Regarding the univariate tested methods, exponential smoothing performed slightly better than ARIMA,
SUMMARY OF THE DIEBOLD- MARIANO STATISTICAL TEST
SUMMARY OF THE DIEBOLD- MARIANO STATISTICAL TEST
Fig. 8. Cumulative MAPE of all tested methods along the whole out-of-sample period. The behavior of prices in these special days is similar to Saturdays and Sundays due to the reduction in
Fig. 8. Cumulative MAPE of all tested methods along the whole out-of-sample period. The behavior of prices in these special days is similar to Saturdays and Sundays due to the reduction in
Fig. 9. Actual and predicted prices of the Holt- Winters’, ARIMA and DR with load methods during two different weeks with holidays (12 October on the left and 25" December on the right). the electricity load as an explanatory variable permits to take into account these calendar effects (see Fig.
Fig. 9. Actual and predicted prices of the Holt- Winters’, ARIMA and DR with load methods during two different weeks with holidays (12 October on the left and 25" December on the right). the electricity load as an explanatory variable permits to take into account these calendar effects (see Fig.
Related topics:
Exponential SmoothingElectric Power SystemsElectricity MarketElectricity pricesElectricity MarketsFunction approximationElectrical and Electronic EngineeringFeed Forward Neural Network
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved