CAPITULO 5 MEJORAMIENTO DE LA PERFORMANCE
5.2. MEJORAS DE CARGUIO Y PERFORACIÓN CON HPGPS
5.2.2. ALTA PRECISION EN PERFORACION
The production forecasts depend on both the hydrological component and the statistical component. The statistical component is controlled by several parameters most influential of which are Quarantine (h), runoff factor, compare, reference period, no. of intervals, max and min value.
The Quarantine parameter specifies the number of hours before the forecast start date that should be ignored, when analysing different model settings, and comparing historical data. a Quarantine is set to 12 hours to simulate the power bidding procedure better. However, while running the model for getting power production forecasts, quarantine is set to 0, and real-time correction is activated to take into account the latest received production data.
The runoff factor parameter specifies how dependant the model is on runoff compared to production when searching for similar intervals. Through multiple observations and comparisons its is observed that when the runoff accuracy is relatively low, a value between 0.4-0.5 will increase the accuracy, while for a good runoff accuracy value of 0.5-0.7 should be used.
The compare parameter sets the period of the intervals in hours, that will be compared to the interval directly preceding the forecast. As mentioned in the previous parameter runoff has significant influence as a predictor, thus for a case of a good runoff accuracy, a period of 6 hours is chosen to while for a low accuracy of runoff 12 hours or more is used. The reason behind an increased period is that the model takes the mean values from these intervals; thus, a more extended period includes more values, which compensate for the low accuracy. While for a good runoff accuracy, a shorter period is used only to include values more recent, and more relevant.
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The reference period parameter sets the historical period in days to look for similar intervals, after several trials, a value of 90 days gave the best increase inaccuracy. Shorter days count might exclude a day of more similar conditions while longer days count might include a day with faulty data.
The no. of intervals parameter sets the no of the most similar intervals to be chosen by the Achelous, setting the value to 30-40 proved enough.
Max and min value sets the limits to the amount of power produced by the power station, småkraft provided a table with the power production ranges for its RORs. However, in some cases, the max value could differ in the historical production, setting the max value parameter to that highest value improved the accuracy of the model. For the min value, through several trials, the findings recommend to set the min value as 0, if it was set to be higher, the model disables the forecast for the hours which are lower than the min value, in turn degrading the forecast accuracy.
Finally, The Achelous automatically disables the hydrological component if it provided poor forecast accuracy, thus its helpful set the max storage parameter to 5.
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7
Results and Discussion
The first step was choosing two hydropower stations (Holmen and Jordalen 2) and investigating their model performance, In order to have a basic understanding of the issues causing lower accuracy. Based on those findings, a recalibration procedure was developed.
The procedure as demonstrated in section 6 gave positive results, as a way to validate the procedure and prove its efficiency it was applied to 5 other hydropower stations Jordal, Usma, Rasdalen, Furegadane and Tverrgjuvlo. The validation introduced new findings that furtherly tuned the recalibration procedure.
After the recalibration, all the models were run and their output data extracted and imported in the accuracy assessment excel file sheet, where a model's accuracy is assessed based on four criteria's; production forecasting, runoff forecasting, precipitation and snow cover. Table 5shows the power production forecast accuracy between the old models and the new recalibrated models.
Table 5 power production forecasts comparison results
Power Station
R2 Production NRMSE Production
Achelous standard Achelous improved Achelous standard Achelous improved Holmen 0.6459 0.7330 0.6507 0.5650 Jordalen2 0.5909 0.6345 0.8766 0.8286 Jordal 0.6686 0.7250 0.4459 0.4062 Usma 0.6864 0.7668 0.6846 0.5903 Rasdalen 0.3260 0.4503 1.1487 1.0374 Furegardane 0.2788 0.3130 1.2776 1.2469 Tverrgjuvlo 0.1265 0.3749 1.4751 1.2478
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What can be understood from the results is that all the hydropower models underwent a considerable increase in their accuracy. Higher NSEC value indicates a better curve fit, while a lower NRMSE value indicates a smaller deviation between the historical and forecasted values. The last three hydropower stations had a relatively large improvement but still not considered accurate.
The inaccuracy of these models was further investigated by comparing the accuracy of the runoff forecasts, the weather forecasts, and snow storage. Table 6 includes the accuracy of the runoff forecasts between the standard models and the recalibrated models. The Achelous model computes the historical forecasts based on the most recent weather forecasts, for the Achelous improved model column, the recalibrated model computed the historical runoff. Therefore Table 8 shows the inaccuracy in the runoff forecasts rather than a comparison between the standard model and recalibrated model performance.
Table 6 Runoff forecasts accuracy results
Power Station
R^2 Runoff NRMSE Runoff
Achelous standard Achelous improved Achelous standard Achelous improved Holmen -0.0927 -0.0337 1.2118 1.4164 Jordalen2 0.0181 -0.0389 1.3129 1.4182 Jordal 0.3309 0.3639 1.0794 1.2463 Usma -0.3241 -0.3265 1.2502 1.4825 Rasdalen 0.6000 0.6230 0.9212 1.0511 Furegardane 0.5710 0.4774 1.2973 1.1678 Tverrgjuvlo 0.5113 0.5414 1.0819 1.2290
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When inspecting the accuracy evaluation results of the last three hydropower stations, the NSEC appears to show high accuracy, and this is not necessarily an indication to an accurate model. Moreover, by inspecting the NRMSE values for the same power plants, the values show poor forecast quality. A logical interpretation of the results is that the deviation between the individual values and the mean value (yi- ӯi) is very large compared to the deviation between the historical and forecasted values (as demonstrated in Equation 5). The NRMSE values show a large deviation between the historical and forecasted values, indicating that the runoff forecasts are unreliable, especially that the forecasted values are compared with the computed values using more recent weather forecasts, rather than actual measured runoff values. This interpretation was supported by counting the 0 values using the excel sheet, where they composed about 25% of the runoff values causing this large deviation.
One approach to investigating the contribution the hydrological component makes to the model's accuracy is to re-run the calibrated models with the hydrological component disconnected.
Table 7 Comparison results for the hydrological component contribution to the model accuracy
Power Plant
R^2 Production NRMSE Production
Runoff connected Runoff disconnected Runoff connected Runoff disconnected Holmen 0.7330 0.7169 0.5650 0.5818 Jordalen2 0.6345 0.6359 0.8286 0.8271 Jordal 0.7250 0.7133 0.4062 0.4148 Usma 0.7668 0.7674 0.5903 0.5896 Rasdalen 0.4503 0.3975 1.0374 1.0861 Furegardane 0.3130 0.2544 1.2469 1.2990 Tverrgjuvlo 0.3749 0.3730 1.2478 1.2498
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From Table 7, it is clear that the hydrological component had little-almost no effect on the total accuracy for the hydropower models with high accuracy. Moreover, for Rasdalen and Furegardane hydropower stations with low forecast accuracy, the hydrological component seems to have an immense contribution to the forecast accuracy.
To further investigate the performance of the hydrological components the historical and forecasted snow cover was compared, this began when a significant deviation in the snow cover values was found, the results of this comparison are mentioned in Table 8.
Table 8 comparison results for the precipitation data and snow cover forecast accuracy
Power Plant
Precipitation Snow cover
R^2 NRMSE R^2 NRMSE Holmen 0.2775 1.3118 0.6003 1.7895 Jordalen2 0.2775 1.3145 0.6049 1.7891 Jordal 0.3257 1.2980 0.6517 1.6168 Usma 0.1609 1.2533 0.5400 1.5685 Rasdalen 0.5463 1.3148 0.5150 2.1927 Furegardane 0.5177 1.2580 0.5048 2.0325 Tverrgjuvlo 0.5159 1.2786 0.4520 2.1550
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In most cases, the historical snow cover is around double the forecasted value, and the same models mainly produced overestimated runoff values. The inaccurate forecasting of snow cover value indicates that the hydrological component fails to simulate the snowfall phenomena, which is explained by the energy budget equation (Equation 8). The energy budget equation with its components dramatically relies on the temperature, which AFS acquires from the weather forecast. Two explanations can be provided for this issue, first is that the temperature forecast is poor, but wrong temperature forecasts cannot extend over the whole winter time (5 months). A second explanation is that the weather stations are located close to the power station which usually lies at an altitude 300-1000 meters (due to Norway's rough terrain) below the average watershed altitude, the temperature can vary within the same watershed. This issue can be surpassed by introducing a new parameter of temperature correction, through which the snow cover curve can be adjusted. Another possible solution is adding an empirical relationship between the temperature and altitude, bridging the weather forecast with the altitude parameter.
Additionally, the volumetric difference between historical and forecasted values, for runoff and snow cover was analysed, as shown in Table 9.
The volumetric difference of runoff represents the percentage difference between the total historical and forecasted runoff volumes of 2019. Moreover, the snow cover is taken as the accumulated value on the last day of the year. Looking at the results here is a significant difference between the forecasted and historical amounts of runoff and snow cover, where the model tends to overestimate runoff and underestimate the snow cover. The underestimation of the snow cover means that the precipitation becomes runoff rather than adding up to the snow cover. The runoff forecast accuracy will significantly improve if the model was adjusted to compute the snow storage in a watershed better.
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Table 9 difference in volume in runoff and snow cover (in mm)
Power Station Historical runoff (mm) Forecasted runoff (mm) diff (%) Historical snow-cover (mm) Forecasted snow-cover (mm) diff (%) Holmen 1242.9 1771.5 42.5 228 91.4 -59 Jordalen2 1242 1770.6 42.5 193 65.3 -60 Jordal 1423.1 1657.8 16.4 137 61 -55.4 Usma 1180.4 1639.5 38.9 243 88.1 -63.7 Rasdalen 1682.4 1936.3 15 125 43.4 -65.2 Furegar- dane 1477.7 1822.3 23.3 193 65.2 -66.2 Tverrg- juvlo 1518.2 1914.4 26 211 61.9 -70.6
Table 10 explains the large difference between historical and forecasted runoff values, where the volumetric difference was calculated from the total historical and forecasted precipitation for each power station. Similar to the forecasted runoff; the Achelous model tends to overestimate the precipitation forecasts as well. Therefore the majority of the excess runoff values can be explained by the error in the precipitation forecasts; unfortunately, in the present time AFS does not include model parameter enabling the user to rectify those errors. However, if the issues in the runoff forecasts were to be corrected, the model would have a significant increase in the production forecast accuracy. Looking at Table 11, if the quarantine parameter were set to 0, the historical comparison would include more recent (more accurate) predictors, this would significantly increase the accuracy of the production forecast. This comparison provides a rough estimation of how the model would perform if it had more accurate
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weather forecasts and was able to forecast runoff and snow cover more accurately.
Table 10 difference precipitation volume between historical and forecasted values (in mm)
Power Station Historical precipitation (mm) Forecasted precipitation (mm) Diff (%) Holmen 1770.9 2197.3 24 Jordalen2 1769.1 2197.3 24.2 Jordal 1851.8 2025.3 9.3 Usma 1710 2037.5 19.1 Rasdalen 2118.4 2324.2 9.7 Furegardane 1973.7 2228.4 12.9 Tverrgjuvlo 2007.9 2278.2 13.4
Table 11 power production forecast accuracy with no quarantine
Power Station
R2 Production NRMSE Production
12h quarantine Recent forecasts 12h quarantine Recent forecasts Holmen 0.7330 0.821848 0.5650 0.461529 Jordalen2 0.6345 0.739251 0.8286 0.699869 Jordal 0.7250 0.797259 0.4062 0.348774 Usma 0.7668 0.857409 0.5903 0.461645 Rasdalen 0.4503 0.602428 1.0374 0.882227 Furegardane 0.3130 0.355688 1.2469 1.20754 Tverrgjuvlo 0.3749 0.5367 1.2478 1.074303
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Prior to AFS, Fortum forecasted the power production numerically, by manually inserting weather forecasts into a simpler model, where the output values are adjusted based on assumptions and relevant experience. This older method is time-consuming and required more personnel. Historical power production forecasts were acquired from Fortum for the period 1/1/2019- 30/9/2019 and then compared with Aiolos forecasts for the same hydropower stations and the same period as mentioned in Table 12.
Table 12 power production forecasts comparison between the older method and AFS
Power Station
R^2 for Production NRMSE Production Old method Aiolos
improved Old method
Aiolos improved Holmen 0.6077 0.7185 0.6023 0.5140 Jordalen2 0.9421 0.6209 0.9380 0.7629 Jordal 0.9731 0.7073 0.4912 0.3757 Usma 0.9942 0.7538 0.5569 0.5116 Rasdalen 0.9539 0.3962 1.2614 1.0061 Furegardane 0.9585 0.2546 1.3415 1.2004 Tverrgjuvlo 0.9745 0.3505 1.5880 1.2275
After inspecting the comparison results, the old method gave a high value for the NSEC. However, AFS provided a relatively significant improvement in the forecast quality when inspecting the NRMSE values. To be more sure of this interpretation, inspection to the power production values was carried out. The inspection found numerous periods where the production values were 0 for a time interval ranging between a day to approximately a week. Missing data, maintenance work, a frozen stream or other reasons can cause the discontinuation in power production. Overall Aiolos showed lower deviation between the historical and forecasted power production, proving to be more reliable than the older method.
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8
Conclusions and Recommendations
By comparing the forecast accuracy of the recalibrated and the standard models (old models), the comparison showed positive results (as shown in Table 5). Next step was to investigate the factors decreasing the model's performance, which included carrying out accuracy assessment analysis on the runoff, snow cover and precipitation forecast accuracy. Assessing the runoff forecasts showed poor forecast accuracy and as runoff is used as a main predictor for the linear regression; this poor forecast accuracy greatly influence the production forecasts. By comparing the total volume of annual historical runoff and forecasted runoff, a significant deviation was found as the model always overestimates the runoff forecasts (see Table 9).
Figure 10 Precipitation forecasts influence on runoff forecasts
Figure 10 demonstrates the influence of the precipitation forecasts on the runoff forecasts; each circle represents one of the 7 RORs taken as a case study. Where the circle circumference represents the deviation in annual runoff forecasts and the dark portion of the circle is an estimate of the error fraction, which is caused by the overestimation of precipitation forecasts. Additionally, the model tends to underestimate snow cover forecasts (see Table 9); this means that some of the precipitation is being computed as direct runoff instead of contributing to snow storage.
Overestimation of Runoff
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Currently, the model lacks the necessary tools for correcting the precipitation and snow cover forecasts. Nevertheless, to provide an estimate of how the production forecasts accuracy would increase the models were run for a quarantine value of 0 allowing the model to use more recent forecasts (more accurate), the production forecast accuracy significantly improved as shown in Table 11.
Collectively AFS currently produces more accurate production forecasts than the previous method used by Småkraft; this was concluded by comparing the forecast accuracy of both methods as displayed in Table 12.
Several recommendations can be drawn from this study which would serve as an upgrade to the model and topics for future studies related to the scope of this thesis. The following recommendation was presented and discussed with the AFS developers:
a) Training the model to adopt actual historical runoff data instead of calculating it using the latest weather forecasts, which will feed the regression model with more sound data for more accurate production forecasts and better calibration for the runoff model.
b) Using more than one decimal for runoff calculations; the model generates forecasts in an hourly interval leading to small values of runoff in (mm), using more than one decimal would decrease the volume deviation.
c) Furtherly develop the hydrological component to be able to forecast peak flows and production from runoff using a conceptual model rather than an empirical equation.
d) Adjusting the precipitation forecasting by adding a new model parameter that is based on bias correction techniques or an empirical relationship, to simulate snowfall better.
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References
Alkarkhi, A. F. & A.A.Alqaraghuli, W., 2019. Chapter 7 - Regression Analysis. In: Applied Statistics for Environmental Science with R. s.l.:Elsevier Science Publishing Co Inc, pp. 113-132.
Chin, D. A., 2013. Water-Resources Engineering. 3rd ed. Edinburg: Pearson education limited.
Cramton, P., 2017. Electricity market design. Oxford Review of Economic Policy, Volume 33, p. 589–612.
esa, 2009. GlobCover. [Online]
Available at: http://due.esrin.esa.int/page_globcover.php [Accessed 28 March 2020].
esri, 2020. ArcGIS tutorials. [Online]
Available at: https://desktop.arcgis.com/en/arcmap/latest/get- started/introduction/arcgis-tutorials.htm
[Accessed 01 03 2020].
FAO, 2013. Land Cover (GLCNMO) - Global version. [Online] Available at: https://globalmaps.github.io/glcnmo.html
[Accessed 29 March 2020].
Geonorge, 2019. Map directory. [Online]
Available at: https://kartkatalog.geonorge.no/metadata?text=DTM%2050 [Accessed 16 March 2020].
Helston, F., 2017. Energy BC. [Online]
Available at: http://energybc.ca/runofriver.html [Accessed 24th February 2020].
Mayer, K. & Trück, S., 2018. Electricity markets around the world. Journal of Commodity Markets, Volume 9, pp. 77-100.
Mays, L. W., 2005. Water Resources Engineering. 2005 edition ed. New york: John Wiley & Sons, Inc..
McCuen, R. H., 1984. Statistical Methods for Engineers. 1st ed. New Jersey: Prentice Hall .
55 NVE, 2019. Map services. [Online]
Available at: https://www.nve.no/map-services/?ref=mainmenu [Accessed 10 March 2020].
Oregon State University, 2020. Interactive learning modules on dam planning and operations. [Online]
Available at: http://rivers.bee.oregonstate.edu/hydropower-production [Accessed 22nd February 2020].
Pidwirny, M., 2006. Fundamentals of Physical Geography, 2nd Edition, the Hydrologic Cycle. [Online]
Available at: http://www.physicalgeography.net/fundamentals/8b.html [Accessed 28th february 2020].
Scottish Government, 2020. Scottish Government. [Online]
Available at: https://www.gov.scot/publications/report-small-scale-hydro- plant-machinery-review/pages/4/
[Accessed 23 february 2020].
Sitterson, J. et al., 2017. An Overview of Rainfall-Runoff Model Types, Washington DC: U.S. Environmental Protection Agency.
Smakraft, 2019. Smakraft as Annual Report. [Online]
Available at: https://smaakraft.no/wp-content/uploads/2019/08/annual-report- smakraft-2018-english-version-final-fully-signed.pdf
[Accessed 16th february 2020].
U.S. Army Corps of Engineers, 1960. Engineering and Design RUNOFF FROM SNOWMELT, Washington: s.n.
Umut Ugurlu, O. A. K. a. I. O., 2018. The Financial Effect of Electricity Price Forecasts' Inaccuracy on a Hydro-Based Generation Company. energies, 11(8), pp. 20-39.
USBR Power Resources Office, 2005. Reclamation Managing water in the west hydroelectric power. s.l.:s.n.
USGS, 2018. USGS. [Online]
Available at: https://www.usgs.gov/special-topic/water-science- school/science/hydroelectric-power-how-it-works?qt-
science_center_objects=0#qt-science_center_objects [Accessed 24 February 2020].
56
Vitec Energy AB, 2020. aiolos forecast studio. [Online] Available at: https://www.vitecsoftware.com/en/product- areas/energy/products/aiolos-forecast-studio/
[Accessed 12 february 2020].
Vitec Energy AB, 2020. User's Guide Aiolos. [Online]
Available at: http://doc.energy.vitec.net/ManualData/ve/en/aiolos/ [Accessed 11th february 2020].
Xie, T. et al., 2019. Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China. Journal of Hydrology, Volume 577.
Zhong, X. & Dutta, U., 2015. Engaging Nash-Sutcliffe Efficiency and Model Efficiency Factor Indicators in Selecting and Validating Effective Light Rail System Operation and Maintenance Cost Models. Journal of Traffic and Transportation Engineering, Volume 3, pp. 255-265.
UNITED STATES. (1960). Engineering and design: runoff from snowmelt. [Washington], [publisher not identified].