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Erdem

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30 Jul 2012 Electricity Load and Price Forecasting Webinar Case Study Slides and MATLAB® code for the day-ahead system load and price forecasting case study. Author: Ameya Deoras

Dear Deoras,
In order to forecast 'balancing system marginal price (smf)' of Turkish electricity market, i used load, day-ahead price (sgof) and the difference between up-regulation volume and down-regulation volume in MWh (nth) as inputs. When i give lagged nth (previous day and previous week same hour nth) only,the forecasting performance is bad, however when i give lagged nth with current nth (i.e kth hour nth for predicting the kth hour smf) forecasting performance is really improved successively. The problem is that forecasting nth for a future hour is a challenging task and the performance of forecasting nth is not so good. I want to give a range of current nth instead of one value (maybe probability dist. of nth / monte carlo) in order to forecast a range of smf instead of one value (probability dist.of smf instead of one forecasting value). But i do not know how to create a range of possible nth values as an input of ANN in order to find forecasted smf ranges. Would you please help me about matlab codes? Below i use the script that is modified by your genpredictor script. I hope that I could describe the problem properly and I would be glad to discuss about it with you. Thank you for your contribution and look forward to hear from you.
Best wishes,
Erdem

function [X, dates, labels] = genPredictorssmf4(data, term)
prevDaySameHourLoad = [NaN(24,1); data.SYSLoad(1:end-24)];
prevWeekSameHourLoad = [NaN(168,1); data.SYSLoad(1:end-168)];
prev24HrAveLoad = filter(ones(1,24)/24, 1, data.SYSLoad);

prevDaySameHoursgof = [NaN(24,1); data.SGOF(1:end-24)];
prevWeekSameHoursgof = [NaN(168,1); data.SGOF(1:end-168)];
prev24HrAvesgof = filter(ones(1,24)/24, 1, data.SGOF);
prevDaySameHoursmf = [NaN(24,1); data.SMF(1:end-24)];
prevWeekSameHoursmf = [NaN(168,1); data.SMF(1:end-168)];
prev24HrAvesmf = filter(ones(1,24)/24, 1, data.SMF);
prevDaySameHournth = [NaN(24,1); data.NTH(1:end-24)];
prevWeekSameHournth = [NaN(168,1); data.NTH(1:end-168)];
X = [data.Hour dayOfWeek isWorkingDay data.SYSLoad prevWeekSameHourLoad prevDaySameHourLoad prev24HrAveLoad data.SGOF prevWeekSameHoursgof prevDaySameHoursgof prev24HrAvesgof prevWeekSameHoursmf prevDaySameHoursmf prev24HrAvesmf data.NTH prevWeekSameHournth prevDaySameHournth];
labels = {'Hour', 'Weekday', 'IsWorkingDay', 'CurrentLoad', 'PrevWeekSameHourLoad', 'prevDaySameHourLoad', 'prev24HrAveLoad', 'CurrentSGOF', 'PrevWeekSameHoursgof', 'prevDaySameHoursgof', 'prev24HrAvesgof', 'PrevWeekSameHoursmf', 'prevDaySameHoursmf', 'prev24HrAvesmf', 'CurrentNTH', 'PrevWeekSameHournth', 'prevDaySameHournth'};
end

30 Jul 2012 Electricity Load and Price Forecasting Webinar Case Study Slides and MATLAB® code for the day-ahead system load and price forecasting case study. Author: Ameya Deoras

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