This white paper explores how we can integrate third-party weather data and utility-specific asset prioritization requirements to build more relevant optimization tools for scheduling the inspection and potential repair of grid anomalies.
Anytime you wish to predict the transient state(s) of something or someone constantly monitored by sensors, time series classifications are the right tool. This article will explain some basic concepts of using deep learning models for TSC and finish with a brief discussion of ways to improve the performance to save on cost and speed.
For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.
Having an autonomous artificial intelligence (AI) system that can monitor individuals via facial mood recognition, vocal tonality analysis, proximity to one another, performance, biosensors, surveys, and more, and predict conflict before it is problematic could improve a unit’s cohesion and performance in missions both in space and in isolated environments on Earth.
Machine learning provides an excellent avenue for predicting future energy consumption. Accurate insights can provide critical insights into variables affecting the demand, providing decision-makers with an opportunity to address these levers. Forecasts also provide a benchmark to identify anomalous behavior, either high/low consumption, and alert managers to faults within the building.
Successfully developing machine learning tools to provide value in a business environment requires much more than algorithmic knowledge and tuning. Machine learning cannot provide value in a vacuum.
MLflow is open-source software initially developed by DataBricks for managing the “machine learning lifecycle.” It makes the model artifacts and their environment specifications more readily available when assembling ML model applications or for other purposes such as collaborating with teammates
Natural language models have come a long way in the past couple of years. With the advent of the deep learning Transformer architecture, it became possible to generate text that could, plausibly, be passed off as written by a human.
To understand GPT-3, it’s helpful to understand a little bit about the history of language models. The language of computers is numbers. The input to all machine learning algorithms is ultimately numbers as well.
Mosaic designed and deployed a custom machine learning model to help this retail energy company predict customer churn and inform a geographic growth strategy.