[01] Farook Sattar, Department of Electrical & Computer, Engineering, University of Victoria, Victoria, Canada.

Energy forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. This paper presents an overview on Big Data and machine learning technology in the context of energy forecasting. The overall objective of Big Data is to discover useful information and knowledge that might otherwise be overlooked or discounted. On the other hand, machine learning helps make complex energy systems more efficient as the systems can learn from a large volume of collected data, detect regular patterns, and optimize its own operations. The energy forecasting plays a vital role to predict energy consumption for large commercial customers. The electrical energy sector is now looking for ways to put a higher level of accuracy and reliability into forecasting electrical loads for the next day and for the next week. It is further complicated by the fact that this sector is dependent on other energy sectors including wind, solar, gas, and hydro, all of which are directly affected by weather events. Therefore, development of intelligent prediction systems based on Big Data as well as machine learning techniques is an emerging issue to pursue a sophisticated and highly tuned decision support system for energy forecasting and it involves integration of various large volume of data from numerical energy prediction models, statistical datasets, real time observations, and human intelligence to optimize forecasts for low-cost energy generation.

Big Data, Energy Forecasting, Machine Learning

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