Mine-Weather 2.0: Our New Cloud-Based Platform for Forecast Verification and Scoring

Macrosoft is proud to announce that we have started building Mine-Weather 2.0, which is entirely cloud-based. This new version of Mine-Weather 2.0 is due to be released in the fall of 2017 this year. Mine-Weather 2.0 utilizes the latest version of Tableau visualization tool that enables creating multi-faceted views of data with visually interactive dashboards. A number of additional advanced scoring algorithms based on statistical methods will be incorporated into the Mine-Weather 2.0 platform.

Mine-Weather 2.0 will be a new high-security cloud-based analytical database and visualization system, designed to allow companies to probe the accuracy of the weather forecasts they use to plan their activities and preparedness in the case of storm conditions. Being cloud based allows users to access data and information directly via a web connection, and can upload and download weather forecast and actual data right from this site. When it comes to configuring the new system for each user, we will configure API’s to inject each client’s weather forecast and actual data in a fully automated fashion. Immediately after configuration, a new user can start exploring the system and all its advanced features.

We will incorporate many additional advanced scoring algorithms for users to dissect the accuracy of their weather forecast data, and determine which forecast is best for their needs and under which circumstances. New enhancements will also include more weather attribute forecasts and more sophisticated forecasting algorithms. In due course of time, we expect to build in direct API’s into the new system that will have the capability facilitating users to receive and send the weather forecast and actual data, and share the same with others on the network.

The key point at the beginning of the implementation of Mine-Weather 2.0 is that even the first utility that signs up can immediately take advantage of the services provided. This should make weather forecast verification easy to understand and evaluate for decision makers, allowing them to judge the confidence in the weather forecasts they are currently using and to evaluate possible new forecasts they are considering to add. Additionally, a number of scoring algorithms based on statistical methods will be incorporated into the Mine-Weather 2.0 platform to make the forecast verification seamless.

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Wholly operated by Macrosoft, Mine-Weather 2.0 makes the process of accessing data during an outage event: easier, more reliable, more accurate and leads to automation. This new enriched-version of Mine-Weather is focused on serving not only the Electric Utility industry but many other industry sectors as well, including municipalities and state governments; federal government; theme park operators; oil and gas operations.

Mine-Weather 2.0 will be operated as a private hosted service, but we are open to all types of other operational models as this service expands its scope.

We will be publishing a general paper on the cloud aspects of the new system, along with a series of three papers laying out all the scoring formulas to be incorporated into this new release.

We refer our readers to the papers on the Mine-Weather web site (www.mineweather.com) for a detailed review of the advanced features and functionality in Mine-Weather 2.0.

 

Please feel free to contact us for further in-depth discussions on Mine-Weather 2.0 and how utilities can take advantage of its enhanced functions.

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About the Author

Dr. Ronald Mueller

Ron is CEO and Founder of Macrosoft, Inc.. Ron heads up all company strategic activities, and directs day-to-day work of the Leadership Team at Macrosoft. Ron is also Macrosoft’s Chief Scientist, defining and structuring Macrosoft’s path forward on new technologies and products, such as Cloud; Big Data; and AI. Ron has a Ph.D. in Theoretical Physics from New York University, and worked in physics for over a decade at Yale University, The Fusion Energy Institute in Princeton, NJ, and at Argonne National Laboratory. Ron also worked at Bell Laboratories in Murray Hill, NJ., where he managed a group on Big Data, including very early work on neural networks. Ron has a career-long passion in ultra-large-scale data processing and analysis including: predictive analytics; data mining, machine learning and neural networks.

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