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Download The use of Enterprise Miner with large volumes of data for forecasting in an automated batch process
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OCS Consulting The use of Enterprise Miner with large volumes of data for forecasting in an automated batch process Presentation to: SeUGI19 - Florence by OCS Consulting & London Electricity May 2001 OCS Consulting recognise all other copyrights and trademarks Agenda OCS Consulting z Overview z Environment and data detail z Enterprise MinerTM software z Model selection z Model refinement z Automation z Summary Overview OCS Consulting z The introduction of NETA has introduced new z z z z challenges to the electricity industry Forecasting supply is particularly testing Energy demand forecasting is subject to a variety of volatile parameters A solution needed to be able to provide fast, accurate forecasts for easy inclusion into the existing systems and software SAS Software identified as providing the optimum solution for the overall system that London Electricity had designed Environment / Data Detail OCS Consulting z Oracle database within UNIX environment z SAS v8.0 with SAS Enterprise Miner™ version 4.0 z Client Server project created in Enterprise MinerTM z Three main areas of data utilised: – demand data – weather data – calendar data z Prediction of demand at the half hourly level, using a stabilised demand value - corrected profile coefficient (CPC) Enterprise MinerTM Software OCS Consulting z Identified because of the ability to control the modelling process z Ease of use and model building z SEMMA methodology z Ease of applying the model code to future data SEMMA Methodology OCS Consulting z z z z z Sample Explore Modify Model Assess z Not all steps are necessarily used - the methodology is completely flexible Statistical Modeling OCS Consulting z Combination of prior expertise / business knowledge and understanding of regression techniques were important z Regression best model because of balance of accuracy and interpretability z Started simple - using basic nodes z Compared further models to substantially improve the initial model Perfecting the Model OCS Consulting z Aimed to improve the original model z Refined the regression within the regression node z Explored further nodes within Enterprise Miner™ z Steps added to the data mining diagram: – Filter outliers node – Group processing node – Score nodes Extraction of Model Code OCS Consulting z Used the Score node to score the data z Extract the score code to for scoring future data z Saved into SAS code file z Incorporated into overnight scheduled environment Moving forwards... OCS Consulting z Though regression proves to be a good model - could try other statistical models: – Neural Networks - could provide an insight into increased model accuracy – Revisit the modelling with Enterprise Miner™ 4.1 with the Time Series Node and model the time dependent data Summary OCS Consulting z Successful regression modelling now incorporated into the forecasting system solution z Data mining process started simple and was refined by supplementing the approach with additional functionality of Enterprise Miner™ z Demand successfully being predicted in the live environment, coinciding with the introduction of NETA in March 2001 Questions OCS Consulting z For further details regarding the presentation: – visit the OCS web-site: www.ocs-consulting.com e-mail: [email protected]
