US-20260127670-A1 - SYSTEMS AND METHODS FOR CONVERTING LIVE WEATHER DATA TO WEATHER INDEX FOR OFFSETTING WEATHER RISK

US20260127670A1US 20260127670 A1US20260127670 A1US 20260127670A1US-20260127670-A1

Abstract

Systems and methods for converting live weather data to a weather index for offsetting weather risk. Weather data source systems generate one or more weather data streams that include weather forecast model and observations data. A data distribution system receives a weather index request, identifies at least one instrument and at least one location associated with the request. Weather risk indication data is extracted among the weather data streams associated with the identified location based on predefined parameters associated with the identified instrument. The extracted data is converted into a set of weather index values corresponding to the location, based on a predetermined algorithm associated with the identified instrument. A weather index presentation package is generated that includes the set of weather index values for distribution to at least one user device. The weather index presentation package being distributed is updated concurrent with changes to weather risk indication data.

Inventors

Stephen John Mitchell
Charles Gabriel Harris

Assignees

INTERCONTINENTAL EXCHANGE HOLDINGS, INC.

Dates

Publication Date: 20260507
Application Date: 20240717

Claims (20)

1 . A method comprising: receiving, by a data distribution system, via at least one user device, a weather index request comprising at least one instrument and a specific geographic location, the data distribution system in communication with one or more weather data source systems via at least one network, the one or more weather data source systems configured to generate one or more weather data streams; selectively extracting, by the data distribution system, weather risk indication data among the one or more weather data streams associated with the specific geographic location and based on one or more predefined parameters associated with the at least one instrument, responsive to the weather index request; converting, by the data distribution system, the extracted weather risk indication data into a set of weather index values corresponding to the specific geographic location, based on at least one predetermined algorithm corresponding to the at least one instrument, the at least one predetermined algorithm comprising a degree day (DD) methodology specific to the at least one instrument and configured to maximize a correlation between the set of weather index values and a consumption value represented by the at least one instrument; generating, by the data distribution system, a weather index presentation package comprising the set of weather index values for distribution to the at least one user device; generating, by the data distribution system, an interactive graphical user interface (GUI) configured for interactively presenting the weather index presentation package to the at least one user device; and updating, by the data distribution system, the weather index presentation package concurrent with changes to the weather risk indication data.
2 . The method of claim 1 , wherein the one or more weather data streams include one or more of weather forecast model data and weather observations data.
3 . The method of claim 1 , wherein the weather risk indication data includes temperature observation estimates associated with the specific geographic location for a predetermined number of sub-periods within a predetermined time period.
4 . The method of claim 3 , wherein the weather risk indication data further includes wind data and radiation data associated with the specific geographic location.
5 . The method of claim 1 , wherein the DD methodology is configured to estimate a weather-based demand for the at least one instrument with respect to the specific geographic location.
6 . The method of claim 1 , wherein the at least one predetermined algorithm includes one or more weights associated with one or more of the specific geographic location, wind data and solar data.
7 . The method of claim 1 , wherein the DD methodology comprises a heating degree day (HDD) methodology associated with a heating demand and a cooling degree day (CDD) methodology associated with a cooling demand.
8 . The method of claim 7 , wherein the one or more predefined parameters include a CDD critical value, a HDD critical value, a CDD exponent factor, a HDD exponent factor, at least one straddle factor and a cancellation factor.
9 . The method of claim 8 , wherein the at least one predetermined algorithm determines the heating demand and the cooling demand for the specific geographic location, an instrument displacement from wind generation, an instrument demand due to seasonal wind generation, an instrument displacement from solar generation and an instrument demand due to passive solar generation.
10 . The method of claim 1 , wherein the at least one instrument is associated with energy instruments including natural gas.
11 . The method of claim 1 , wherein the set of weather index values is configured to offset a weather risk of the at least one instrument.
12 . The method of claim 1 , wherein the at least one instrument is associated with one or more of energy, agriculture, retail, fixed income, and equity markets.
13 . The method of claim 1 , further comprising: determining one or more of at least one estimated settlement value and at least one actual settlement value of the set of weather index values.
14 . The method of claim 1 , further comprising: presenting, in one or more windows of the interactive GUI, one or more of a historical weather data region, a weather forecast region specific to the specific geographic location, a weather forecast map region, a user input region and an index-specific weather index region.
15 . The method of claim 14 , wherein the index-specific weather index region further comprises one or more of an index settlement value region, a weather index value region, a climatological value region, one or more bias correction tools and one or more performance weighting tools.
16 . The method of claim 1 , wherein the set of weather index values includes at least one indication of a departure from a predetermined weather index value associated with the at least one instrument.
17 . The method of claim 1 , further comprising: selecting, by the data distribution system, the at least one predetermined algorithm among a set of predetermined algorithms associated with plural instruments.
18 . A non-transitory computer readable medium storing computer readable instructions that, when executed by one or more processing devices, cause the one or more processing devices to perform the functions comprising: receiving, via at least one user device, a weather index request comprising at least one instrument and a specific geographic location; selectively extracting weather risk indication data among one or more weather data streams associated with the specific geographic location and based on one or more predefined parameters associated with the at least one instrument, responsive to the weather index request, the one or more weather data streams generated by one or more weather data source systems; converting the extracted weather risk indication data into a set of weather index values corresponding to the specific geographic location, based on at least one predetermined algorithm corresponding to the at least one instrument, the at least one predetermined algorithm comprising a degree day (DD) methodology specific to the at least one instrument and configured to maximize a correlation between the set of weather index values and a consumption value represented by the at least one instrument; generating a weather index presentation package comprising the set of weather index values for distribution to the at least one user device; generating an interactive graphical user interface (GUI) configured for interactively presenting the weather index presentation package to the at least one user device; and updating the weather index presentation package concurrent with changes to the weather risk indication data.
19 . The non-transitory computer readable medium of claim 18 , wherein the one or more weather data streams include one or more of weather forecast model data and weather observations data.
20 . The non-transitory computer readable medium of claim 18 , wherein the weather risk indication data includes temperature observation estimates associated with the specific geographic location for a predetermined number of sub-periods within a predetermined time period.

Description

TECHNICAL FIELD The present disclosure generally relates to improving data structure distribution and, in particular to distribution systems, interactive graphical user interfaces (GUIs) and methods for the integration of disparate data structures for interaction and meaningful information about the combination of disparate data types, including real-time integration of weather and market data structures and real-time weather indices. BACKGROUND Problems exist in the field of digital distribution platforms. In general, a digital distribution platform may manage digital data content (e.g., digital goods, digital information, etc.) and distribute the content to various end-users. Conventional platforms may distribute digital data content from one or more data sources (e.g., data feeds, data files, user input and the like) that may be distributed across one or more networks, may include different data types, different data formats, different data communication requirements, different network security, different availability time periods and the like. Moreover, distribution platform may distribute data content in one or more distribution formats (e.g., in a data file, on a user interface, in a spreadsheet and the like), to particular end-users with varying amounts of data and/or personalized data and the like. Conventional platforms also exist that may provide the ability for user-interaction with the distributed data (e.g., data analysis tools, actions that may be performed with the data and the like) in addition to the presentation of distributed digital data content. All of the above variables associated with data distribution make it technically difficult to manage data distribution and interaction for real-time distribution. Yet further, distribution of digital data content in real-time becomes increasingly difficult as the volume of digital data content to be distributed increases and/or as the digital data content changes more rapidly over time (e.g., with increasing volatility of the data content). For example, it may become increasingly technically difficult for a distribution platform to continually update an interactive user interface with the most up-to-date data content, when the data volume increases and/or the data content itself changes rapidly. In such instances, any transmission delays over one or more networks to obtain the data content coupled with any data handling delays by the distribution platform for handling the received data content (e.g., to convert a data format of received data content, to normalize any data content, to remove any data content not suitable for presentation, to generate data for distribution in one or more distribution formats, create aggregated output data, generate any user interfaces and the like) may introduce significant errors in distributed data and the ability by the end-user to interact with the distributed content. For example, the distributed data content may not provide the most up-to-date information, leading to a situation in which a user may perform an action based on stale information (e.g., an auction for an object at a price that no longer exists, respond to older data content when newer content exists, etc.). Another significant technical problem that still exists in data distribution platforms includes the integration of data content for distribution that includes disparate data types. For example, while it may be possible to simply display disparate data types on user interface side-by-side, it may be difficult to integrate disparate data types in an intelligent manner, e.g., where the integration of the disparate data types provides meaningful information about the combination of disparate data types. For example, a first data type may include data values that are based on one set of underlying index values whereas a second (different and independent) data type may be based on a completely different and unrelated set of underlying index values. Thus, it may be technically difficult to suitably align and integrate disparate data types. It may be still more difficult to integrate disparate data types with real-time, rapidly changing data. Accordingly, there is a need for a system (including a novel interactive GUI), and method for integrating and distributing disparate data types in a fully-automated (or near fully-automated) manner. All of this, without significant increases to the computational burden, cost, system complexity, re-programming requirements and system maintenance. SUMMARY Aspects of the present disclosure relate to systems, methods and non-transitory computer readable mediums for converting live weather data to a weather index for offsetting weather risk. A system includes one or more weather data source systems and a data distribution system in communication with the one or more weather data source systems via at least one network. The one or more weather data source systems are configured to generate one or more weather data str