Deep Learning for Time Series Modeling
نویسندگان
چکیده
Demand forecasting is crucial to electricity providers because their ability to produce energy exceeds their ability to store it. Excess demand can cause “brown outs,” while excess supply ends in waste. In an industry worth over $1 trillion in the U.S. alone [1], almost 9% of GDP [2], even marginal improvements can have a huge impact. Any plan toward energy efficiency should include enhanced utilization of existing production. Energy loads provide an interesting topic for Machine Learning techniques due to the availability of large datasets that exhibit fundamental nonlinear patterns. Using data from the Kaggle competition “Global Energy Forecasting Competition 2012 Load Forecasting” [3] we sought to use deep learning architectures to predict energy loads across different network grid areas, using only time and temperature data. Data included hourly demand for four and a half years from 20 different geographic regions, and similar hourly temperature readings from 11 zones. For most of our analysis we focused on short term load forecasting because this will aide online operational scheduling.
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