Following the success of mathematical and statistical modelling in various financial markets, we believe that quantitative methods can also be used to effectively establish trading vehicles for power and its derivatives. However, most of the quantitative literature in power markets is focused on specific aspects primarily from the perspective of load-serving or generation units. Instead, we aim to build a quantitative power trading framework which expands the activities of Plant-E Corp in North-American power markets and fills in the current gaps within the literature.
Machine learning techniques have been applied to the financial industry for some time. They have allowed large utilities and generators to better forecast their needs, and the prices they will pay, leading to a generally more efficient grid. However, very little research has been done that could benefit power marketers, who do not have a load to serve or a generating facility to manage. The application of machine learning techniques has yielded great results in the financial industry.
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