GB2530086A - System and method for remotely optimising control algorithms and rulesets on a network server or using cloud based services - Google Patents

System and method for remotely optimising control algorithms and rulesets on a network server or using cloud based services Download PDF

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Publication number
GB2530086A
GB2530086A GB1416200.2A GB201416200A GB2530086A GB 2530086 A GB2530086 A GB 2530086A GB 201416200 A GB201416200 A GB 201416200A GB 2530086 A GB2530086 A GB 2530086A
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server
schedule
controller
master
data
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GB201416200D0 (en
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Michael John Bradley
James Laurence Taylor
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00Adapting or protecting infrastructure or their operation
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

A server comprises an input/output port for sending and receiving data via a network, a data storage device and a controller for maintaining a master schedule for energy management. The server utilises sources such as smart building sensors, energy meter readings, lighting systems, heat and ventilation control systems, building usage profiles, energy targets, predictive services such as weather forecasts, energy tariffs and energy availability from alternative renewable sources in order to determine an optimised schedule. The usage profile may include room occupancy calendars, movement detectors, security systems, calendars, temperature set points and profiles, lighting themes and profiles, social data and events diaries. The controller maintains the master schedule, which may be updated over the network. A sub-portion of the master schedule may be translated into a basic schedule with at least one day less than the master schedule and delivered to a controller.

Description

System and method for remotely optimising control algorithms and rulesets on a network server or using cloud based services.
Description
BACKGROUND
The domain in which we are focused is Energy Management (EM), but our innovation and solution applies to everything from transport thru to financial markets, anywhere where algorithms are used. EM is a very complex area with a huge amount of work being done on energy generation (centralised and micro-generation), distribution (Smart Grid) and consumption (metering, load control, cost/consumption reduction).
At the same time, related domains are experiencing similar intense development.
Machine-to-Machine (M2M) communications, Internet of Things (loT), Internet connectivity, building automation, adaptive (smart) technologies and internet technologies (including data protection and security) are all converging with impact on the EM domain.
EC Mandates (M/441 Smart Metering, M/490 Smart Grid and M/480 Building Energy Performance) and their European standards co-ordination groups (SM-CG, SG-cG) as well as international standards bodies (e.g. lEc, IEEE...) and energy standards groups (e.g. SGIP, SEAD...) are producing guidelines, collaborations and standards for interoperability of EM components and services to reduce costs and CO2 emissions and improve energy generation and distribution efficiency EC studies in DG CNECT and ENERGY have identified key actions including direct funding of groundwork for common M2M data model and standards (TNO ontology and ETSI smartM2M data model).
The old vertical model of each provider delivered entire vertical solutions is being replaced by a new horizontal vision of each provider delivering it own solution layer from data capture thru to analytics and applications. Nobody does everything it about interoperability and building an ecosystem where all applications work with all services.
The vast majority of these products and services focus on providing information to consumers to allow them to identify opportunities for energy saving. Product examples include energy consumption displays (sometimes linked to tariff information), mobile applications to monitor and remotely control environment (heating/cooling, security and access) with a smaller number of adaptive systems (Nest learning thermostat') at the leading edge.
Historically, consumers have not been very willing to pay for home controllers', even at low cost with clear return on investment benefits. consumers expect such
I
functions to be provided as an integral part of some other equipment or service (e.g. the central heating controller and its thermostat).
Professional market equipment tends to be proprietary as manufacturers protect their customer bases from intrusion from competitors. To date, each product range has incorporated its own control function and the interoperation of products from multiple vendors has been limited. Organisations such as the ZigBee Alliance have emerged to expand markets for sensors and control devices, but have so far limited their specifications to device interconnection and data exchange. The Smart Energy Profiles (SEP 1 and SEP 2) extend the application to include EM specific data models including variable pricing.
In the context of this complex background, our innovation focusses on the problem of optimisation.
Now we have discoverability of sensors and access to the data, decisions can be made. This is usually being performed by a simple controller with limited data using simple control theory and rules. Normally a mixture of a set point with upper and lower limits, and an ON/OFF timer sequence (schedule). Rarely does the user interact with the system.
To maximise the benefits of this data, decisions have to be made. The solution is to combine machine-browsable service discoverability (using open standards), with an algorithm optimisation process so that decisions can be made dynamically and automatically. Supported by a live real-time update and monitoring service to maintain optimal control with the potential to use live streamed and historical data.
In order to support a large scale service business model, the Abstraction Service must be able to automatically convert the local rule set to a standard data model representation which can be input to the OS and via which the optimised rule set can be returned to the local controller without manual intervention.
This abstraction process will learn about the system and the resultant output of this analysis is the Master Control Schedule (MCS). This will include the system topology (architecture), data points (sensors and digital inputs), algorithms, sequences, rulesets, set points and schedules either on the local network server or via a cloud based server.
This will then go thru a series of optimisation processes. The resultant output (MCS) will then be downloaded back onto the control network or local controller.
This will then be continuously monitored using heuristic algorithms to learn user behaviour and to adapt the control operations using exogenous data (weather service, traffic reports, room occupancy via sensors and schedules). Interoperate with other systems such as lighting and H&V.).
Figure 1 -System overview and data flow * Based on the successful FP7 project AGILE, a general algorithm exists for optimising large scale control systems (LSCS). The question is how to apply such an algorithm to EM in an effective way. The universal problems of greenhouse effect, environmental change and rapidly escalating energy prices are all addressed (in part) by better control systems delivering (even better) reductions in energy consumption via optimisation of individual EM systems.
* Optimisers must be trained on data sets from the system being controlled -sensors and control point settings for actuators.
* New generation devices are employing heuristic algorithms to learn user behaviour and to adapt the control operations in some manner beneficial to the human user (e.g schedule heating/cooling optimally for a given user pattern of occupancy). A small number of systems optimise entire environments including external influences (e.g. AGILE use cases).
SUMMARY
Currently control systems require the user to make the decisions as to set points and schedules. They may have some adaptive elements such as intelligent temperature sensors but they still require user input to control the system. The whole purpose of the optimising controller is to make the decisions for the user in a timely manner using an optimising service that uses all the available relevant data to set and maintain the Master Control Schedule (time based schedules, rule sets, set points, algorithms and topology of the system(s)) for the controller(s) in a network based system. This network based service can be run on local servers or in the cloud. It's envisaged that the optimising service will have access to external knowledge bases such as a weather forecasts, energy tariffs, as well as key system data, usage schedules and building target profiles. In order to support a large scale service business model, the Abstraction Service must be able to automatically convert the local rule set to a standard data model representation which can be input to the Optimisation Service and via which the optimised results can be returned to the local controller without manual intervention.
Our solution provides for: 1. System initialisation using an abstraction service to extract the system topology, data points, algorithms sequences, control logic (ladder and state), rulesets, set points and schedules (dynamic and time based) either on the local network server or via a cloud based server. This process produces the Master Control Schedule.
2. Optimisation service to optimize the data of 1 3. Continuous real time service that continues the optimisation process according to a schedule or ruleset or any other command and control structure.
4. Use of heuristic algorithms to learn user behaviour and to adapt the control operations.
5. Use of external data such as weather service, traffic information, diaries usage schedules (room occupancy, events planner), emergency inputs and links via the internet to other servers.
6. Automatic system expansion using the above allowing for the addition of extra sensors, controller and host services

Claims (20)

  1. Claims The invention claimed is: 1. A server accessible over a computer network or via cloud based services running the Optimiser Service. This service will use data from a variety of sources such as but not limited to smart buildings sensors, energy meter readings, lighting system(s), H&V control system(s), building usage profile(s) (room occupancy calendars, movement detectors, security system, calendars, temperature set points and profiles, lighting themes and profiles, social data, events diary), energy targets (maximum usage or cost, load shedding), may use predictive services (such as weather forecasts, ), energy tariffs, energy availability from alternate energy sources such as Wind or Solar power, tidal energy.The Optimising service will then determine a Master Control Schedule (this includes but is not limited to a time based schedule, sequences, rules, algorithms and set points). Which will then be automatically downloaded to the controller or network based server. There will be a capability for user input and override capability in case of an unforeseen event.the server comprising: an input/output port for sending and/or receiving data via the computer network; a data storage device; a controller coupled to the input/output port and the data storage device, the controller (or local host / server) configured to: maintain a master schedule having up to 366 days, the master schedule being capable of being updated by an Optimising Service or user over the computer network via the network port of the server; translate a sub-portion of the master schedule into a basic schedule, wherein the basic schedule has at least one less day than the master schedule; and deliver the basic schedule to a controller over the computer network via the network port of the server.This server can be networked another network and/or to a series of controllers which it monitors and manages.
  2. 2. The server of claim 1 where the Master Control Schedule encompasses all of the computer control elements and data points, including but not limited to the system topology, algorithms sequences, control logic (ladder and state), rulesets, set points and schedules (dynamic and time based).
  3. 3. An abstraction process for the server of claim 1, to extract all the data required to reconfigure and set up and initialise the algorithms, system topology and sensor data using a machine discoverable process. This Master Control Schedule will then be downloaded to the server and/or controller.
  4. 4. The server of claim 1, wherein the controller is further configured to deliver an updated basic schedule to the controller according to a predetermined schedule.
  5. 5. The server of claim 1, which is continuously monitored and updated in real time.That continues the optimisation process according to a schedule or ruleset or any other command structure.
  6. 6. The server of claim 1, utilising heuristic algorithms to learn user behaviour and to adapt the control operations.
  7. 7. The server of claim 1, utilising exogenous data such as weather service, traffic information, diaries, usage schedules (room occupancy, events planner), and emergency inputs.
  8. 8. The server of claim 1, an automatic system expansion process using the above allowing for the addition of extra sensor(s), controller(s) and host services.
  9. 9. The server of claim 1, wherein the updated basic schedule covers at least one day not covered by the previously delivered basic schedule.
  10. 10. The server of claim 1, wherein the controller is further configured to deliver an updated basic schedule to the controller in response to an update to the master schedule.
  11. 11. The server of claim 1, wherein the update to the master schedule is a result of a user updating the master schedule over the computer network.
  12. 12. The server of claim 1, wherein the controller is further configured to deliver an updated basic schedule to the controller upon restoration of a lost computer network connection between the controller and the server.
  13. 13. The server of claim 1, wherein the controller is further configured to: receive scheduling data from a third party calendar application over the computer network; translate at least a portion of the scheduling data received from the third party calendar application into the master schedule.
  14. 14. The server of claim 1, wherein the master control schedule is associated with a user account.
  15. 15. The server of claim 1, wherein the controller is further configured to: implement a web application, wherein the web application is configured to display one or more web pages viewable from a remote location via the computer network, wherein one or more of the web pages provide a virtual user interface for the controller.
  16. 16. The server of claim 1, wherein the basic schedule covers at least one day.
  17. 17.The method of claim 1, wherein the basic schedule comprises at least one schedulable time period per day, where each time period has a corresponding operating set point.
  18. 18. The server of claim 11, wherein the master schedule includes thirty days or more for scheduling operation of the system.
  19. 19. The abstraction process could be run from a Portable Computer (PC) connecting via the local network or directly onto a communications port of the controller.
  20. 20. The server of claim 1 which is networked to a cluster of other servers networked to other systems and controllers.
GB1416200.2A 2014-09-12 2014-09-12 System and method for remotely optimising control algorithms and rulesets on a network server or using cloud based services Withdrawn GB2530086A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
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US10401039B2 (en) 2017-02-28 2019-09-03 Ademco Inc. Evaluation of heating liquid pressure drops in a hydronic heating system

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US20070045431A1 (en) * 2005-08-31 2007-03-01 Ranco Incorporated Of Delaware Occupancy-based zoning climate control system and method
US20090240381A1 (en) * 2006-03-24 2009-09-24 Rtp Controls Method and apparatus for controlling power consumption
US20110184565A1 (en) * 2010-01-22 2011-07-28 Honeywell International Inc. Hvac control with utility time of day pricing support
US20120232969A1 (en) * 2010-12-31 2012-09-13 Nest Labs, Inc. Systems and methods for updating climate control algorithms
US20130226359A1 (en) * 2012-02-27 2013-08-29 Siemens Corporation System and method of total cost optimization for buildings with hybrid ventilation
US20130274940A1 (en) * 2012-03-05 2013-10-17 Siemens Corporation Cloud enabled building automation system
WO2014152301A2 (en) * 2013-03-15 2014-09-25 Nest Labs, Inc. Systems, apparatus and methods for managing demand-response programs and events

Patent Citations (7)

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Publication number Priority date Publication date Assignee Title
US20070045431A1 (en) * 2005-08-31 2007-03-01 Ranco Incorporated Of Delaware Occupancy-based zoning climate control system and method
US20090240381A1 (en) * 2006-03-24 2009-09-24 Rtp Controls Method and apparatus for controlling power consumption
US20110184565A1 (en) * 2010-01-22 2011-07-28 Honeywell International Inc. Hvac control with utility time of day pricing support
US20120232969A1 (en) * 2010-12-31 2012-09-13 Nest Labs, Inc. Systems and methods for updating climate control algorithms
US20130226359A1 (en) * 2012-02-27 2013-08-29 Siemens Corporation System and method of total cost optimization for buildings with hybrid ventilation
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Publication number Priority date Publication date Assignee Title
US10401039B2 (en) 2017-02-28 2019-09-03 Ademco Inc. Evaluation of heating liquid pressure drops in a hydronic heating system

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