Whether or Knot LLC

Ecologic Analytics, LLC

Vaisala Oyj

Landis+gyr Analytics, LLC

David James Hubbard

Craig Allen Mataczynski

Venkatesh Ramachandran

John Skog

Bruno Chabot

Seth Jacob Miller

Tony Lee Huffman

Anil Kumar Gupta

Jerome Frank Winzig

Nicholas Craig Shipes

In one example, a method of generating and delivering environmental data includes receiving a request for requested data corresponding to a first environmental characteristic and a second environmental characteristic. The method includes retrieving a first set of data corresponding to the first environmental characteristic. The method includes retrieving a second set of data corresponding to the second environmental characteristic. The method includes generating an image having a first channel and a second channel. The values of the first channel correspond to the first set of data. The values of the second channel correspond to the second set of data.

Systems and methods for variable multi-channel and multi-dimensional data generation and transmission

In one example, a computer-implemented method of generating a weather product is disclosed that comprises receiving a product generation input, the product generation input being indicative of a weather product. The method further comprises retrieving a weather data point, based on the product generation input. Further, the method comprises analyzing the weather data point with a weather analysis component to identify weather parameters relevant to the weather product. In addition, the method comprises generating the weather product to include the identified relevant weather parameters.

Systems and methods for weather data distribution

A computer-implemented method is disclosed that comprises receiving, at a data provider computing system, a data contribution from a client device associated with a user, and based on the received data contribution, determining a user identifier that uniquely identifies the user. The method comprises performing a data validation operation to validate the data contribution. The method comprises, based on validating the data contribution, storing the data contribution in association with the user identifier. Further, the method comprises receiving a data request from a data consuming system. Based on the data request, the stored data contribution is identified and distributed to the data consuming system. Based on distribution of the data contribution to the data consuming system, a distribution value associated with the data contribution is generated. An indication of the distribution value is stored in association with the user identifier.

Systems and methods for electronic data distribution

A method for processing daily consumption, demand and time of use meter reads for electric, gas, water and other metered entities. The meter reads are collected by any of the well known Automated Meter Reading (AMR) technologies and loaded into a meter data warehouse. At a minimum daily meter reading, reads that are tagged as Good, Stale, Partial, Incomplete and Missing are required for properly identifying usage patterns, applying rules for error patterns and estimating the reads which will provide quality meter reads to the utilities thereby identifying meter problems before they become billing problems. The overall process involves loading the daily meter reads into a database, comparing each meter read against its previous day's read and applying a set of rules that help in validating, editing and estimating (VEE) of this data. The VEE rules that are applied can be broadly classified into five (5) categories. They are 1) Cumulative (CUM) Read Error Detection 2) Demand (DEM) read Error Detection 3) Rounding, Truncation and Offset rules 4) Allocation and Estimation 5) Others. Once these rules have been applied and the required reading estimated and edited, the data is now ready for billing. Prior to the reads being used for billing, another process is executed that helps to determine the meters that need to be replaced and hence whose reads cannot be used for billing as it would result in the customer being billed for incorrect usage. The output of this process can be customized to suit any of the popular Utility Billing Systems (UBS)'s input format and mimics a meter exchange transaction.

Method for validation, estimation and editing of daily meter read data

Many public utilities companies employ meter data management systems (MDMSs) to manage the millions and sometimes billions of daily and interval meter read values that they collect during a single billing period. One problem with some MDMSs estimate missing daily reads based on average daily usage and these estimates do not match daily usage values determined from interval read data, causing sum check exceptions. Such exceptions are undesirable because they can lead to billing errors and lost consumer confidence in the utility, among other things. To address this, the present inventors devised an exemplary system that uses good and estimated interval read data to more accurately estimate the missing daily reads and thereby reduce occurrences of sum check exceptions. The exemplary system also estimates missing time of use and demand data based on interval read data.

Systems, methods and software for adjusting read data from utility meters

Method and system for validation, estimation and editing of daily meter read data

Growing community of inventors

Chaska, MN, United States of America

David James Hubbard