Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where electricity prices become more volatile and less predictable using classical approaches. The Locational Marginal Pricing (LMP) pricing mechanism is used in many modern power markets, where the traditional approach utilizes optimal power flow (OPF) solvers. However, for large electricity grids this process becomes prohibitively time-consuming and computationally intensive. Machine learning (ML) based predictions could provide an efficient tool for LMP prediction, especially in energy markets with intermittent sources like renewable energy. This study evaluates the performance of popular machine learning and deep learning models in predicting LMP on multiple electricity grids. The accuracy and robustness of these models in predicting LMP is assessed considering multiple scenarios. The results show that ML models can predict LMP 4-5 orders of magnitude faster than traditional OPF solvers with 5-6\% error rate, highlighting the potential of ML models in LMP prediction for large-scale power models with the assistance of hardware infrastructure like multi-core CPUs and GPUs in modern HPC clusters.

As AI and machine learning have regained popularity over the last two decades, so has an interest in their application to financial prediction tasks. The two seem like a natural fit as data generated by markets have been scrutinized by investors for over a century in hopes of forecasting their way to financial success. A casual survey of the associated literature reveals there are generally two broad approaches to the topic. In one corner sits the astute STEM practitioners who view the task at hand as an engineering problem, preferring complex and novel architectures that minimize a nominated error metric. Whereas in the opposite corner resides the learned financial practitioner, who remains innately cognizant of efficient markets (EMH) and the need for explainability, in doing so, favoring simpler models infused with domain insights.

The following post was sponsored by Cisco. The opinions, thoughts and observations, however, are completely my own. Last month I wrote on the challenges placing pressure on the CIO and IT leaders to move from an operations-focus to an opportunity-focus. According to ZK Research, 78 percent of today's IT budget is spent on "running the business," leaving very little to invest in innovation. With the mounting pressure to innovate and drive business growth, is it doom and gloom for IT leaders trying to keep pace with moving targets and c-suite expectations?

Vivifi India Finance Private Limited (Vivifi), a FinTech NBFC (non-banking finance company), has transformed the consumer credit landscape in India with its artificial intelligence (AI) driven loan management system used by its flagship lending product – FlexSalary. FlexSalary is a reusable line-of-credit with flexible repayment options which enables access to credit to the sub-prime population of India. FlexSalary's AI powered underwriting algorithm plays a crucial role on two fronts – first, eliminate fraud, and second, by minimizing human intervention, reduce the loan approval process by a fourth. "Artificial Intelligence and Machine Learning minimize human intervention in the loan approval process. So, not only does FlexSalary enable credit availability anytime, anywhere provided one has an internet connection and a bank account, but with its revolutionary Electronic Income and Bank Verification option, it allows an individual to receive the loan on the very same day whereas traditional avenues take much longer," said Anil Pinapala, Founder & CEO, Vivifi India.