DHI Blank PowerPoint Presentation UK content/presences/emea/uk... · © DHI #4 Needs •Overview...

transcript

Forecasting technologiesJohan Hartnack

Forecasting

• Decision makers

• Operator/system maintenance

• Public

• Overview with quick assessment of

operational choices and impacts

• Configuration and maintenance.

Advanced operations of system

• Clear messages to be understood

without specialist knowledge

Decision maker

• Evaluate choices quickly

• Impact assessment

• Issue warnings

• Initiate mitigation measures

Operator/system maintenance

• Model set-up

• Uncertainty assessment

• Scenario evaluations

• Configuration

• Data hook up

Dissemination

• GIS

• Time series

• Temporal development

• Levels of data access

Configuration and operating system

• Model adaptors configuration through XML files

• Workflow - Jobs manager

• Create automatic reports

Openness as you need it – empowering you

Spreadsheets

• Well known spread

sheet functionality

• Access live data

• Build models

• Create reports

seriesGIS Spreadsheet Scenario Jobs Indicator

Analysis

MCA/CBADocument Metadata

Scripting

• An Iron Python

environment

• API to all modules

• Functionality to store

and manage scripts

Code your own Tools

• Develop your own

• API Access to data

• Customization

Open Software Architecture

MIKE Operations

MIKE Workbench

Desktop WEB

Data base

GIS Time-series Scripting Spreadsheets Jobs

Workbench

MODELS

INFOMIKE

PLANNING

OPERATIONS

MIKE Technologies for all Water Environments

• Packaged as standard products

• Configurable

• Open (e.g. to data)

• Extendable (scripting, API)

Aarhus – Danish for Progress

… Architecture

and art

… Business and

industries…Aarhus

University

…Recreational

and active use of

the environment

Expected project outcome

…Improved water

quality/partly bathing water

in River Aarhus

… Bathing water in the

Harbour (hygienic)

…Bathing water in Lake

Brabrand (hygienic)

MIKE 11MIKE URBAN

MIKE 3MIKE SHE

Data flow

Model preparation

Model execution

Real time control

Issues of warning

Distributed

rainfall

from Radar

Automated Integrated Modelling

Layer 3

PLC/SCADA

Sensors/Actuators

Short term rainfall

forecast (MAR) Dynamic Overflow

Risk Analysis

(DORA)

WWWTP max.

hydraulic load

Predicted run-off

(MIKE URBAN)

Data validation and

filtration

Software sensors:

Flow, Elevations,

tank filling, etc.

PID (flows) at each

storage tank

PID (elevations) at

each storage tank

PID output: 0-100%

distributed to

setpoints for pumps,

weirs/gates at each

storage tank

Layer 2

Layer 1

Levels, flows and weir/gate positions Set-points

DIMS.CORE

Real-Time Integrated Control

DHI bathing water forecast service

• App and web based public warning system

Public information on bathing water quality

Internet

Environmental Section

Aarhus Municipality

Aarhus Water

Utility company

Waterforecast

Operated by DHI

One warning system -Integrating data from multiple organizations and authorities

Project Status

…Improved water quality in

River Aarhus

… Bathing water in the

Harbour (hygienic)

…Bathing water in Lake

Brabrand (hygienic)

Saving in investment

• Ordinary and larger retention basins 79 million EUR

• Controllable and smaller retention basins 45,6 million EUR

• Automation and control system 1,7 million EUR

• Total 47,3 million EUR

• Saving 32 million EUR

Simple and effective web applications for

real time monitoring and early warning

systems

Greve – Flood Warning System

Web solution

MIKE Models

Rainfall forecast

MIKE21 FM

MIKE URBAN CS

MIKE OPERATIONS

Automatic Operation

Results every 4 hours

Forecast 24 hours

Web solution based

on MIKE products

MIKE Web technology

Time-series

Spreadsheets

MIKE Web API

Website

Polymer/Web

Components

Configurable MIKE product Project specific implementation

MINERWA- Minimising Non-Revenue Water in distribution networks

“NRW is produced water, which

is not paid for. It can be due to

real losses (like leakages) or

apparent losses (such as theft or

metering inaccuracies). In many

parts of the world, water

resources are limited and thus

NRW can have significant

consequences on the level of

service as well as on revenue

LONG-TERM SOLUTION TO SUSTAINABLE

WATER NETWORK MANAGEMENT Our solution to Minimise Non-Revenue Water (MINERWA) works with the data you

have, follows international recommendations and uses a well-proven methodology

– and it pays off from the start. Only a minimum of input data is required to

establish an overview and understanding. MINERWA offers:

• a well-structured data repository

• analytical engines

• an efficient, yet customisable user-interface with key performance indicators as

well as in-depth reporting

“MINERWA is a solution

delivered jointly by DHI and

EnviDan International. It is

offered as a hosted solution

where we take full responsibility

of running and maintaining the

MINERWA and making it

available to you

MINERWA benefits • reduced water losses

• reduced pipe burst risks

• reduced energy consumption

• Documentation of effects

• Facilities for for rehabilitation and emergency planning, water quality risk

analysis and much more.

Minerwa web solution

How to build your own integrated water

management system

Building blocks – an example

Data management system

•Data integration/ validation

•Processing

•Reporting

•SCADA

Model analysis

•Numerical / empirical models of the system

Operationalzation

•Scheduling and coupling of data management system and models

Control / optimization

•Control

•Optimization

•Feedback to SCADA

•Manually

•Automatic

Warning

•Operational people

•Public

Our solution builds on generalised

software components to provide

standard products as well as

custom solutions

DHI Software frame work for integrated solutions

• Integration of data and models

− MIKE OPERATIONS

• Data management and integration to SCADA

− DIMS.CORE

• Numerical models

− MIKE URBAN (collection system)

− MIKE 11 (River)

− MIKE SHE (Catchment + river)

− MIKE 3 (Harbour and ocean)

Looking ahead

• Data

− Assimilation techniques

• Faster model execution

− Hardware

− Smart systems

− Surrogate models

Assimilation to internal measurements

Data Assimilation Framework

• Kalman filter state updating procedure

• Introduction of boundary errors in each simulation

of the ensemble

• Ensemble statistics

• Facilitates state updating on a wide range of

dependent variables

• Uncertainty analyses

Traditional MIKE model

MIKE model

Time series Topography Model parameters

Water level,

Discharge,

Velocities,

Future

Statistics:

Mean values,

Covariance,

Confidence intervals

Results

Simulation

Uncertainty Uncertainty Uncertainty

Data assimilation

Measurements

Data assimilation

Mathematical setting

Model description in the form of a model operator

xk The state variables of the system (H- ,Q- , depth integrated velocities)

F The model operator (a time step in MIKE HYDRO River)

k the time step

uk The forcing terms of the system (boundary conditions)

),(1 kkk uxx F

Model errors

− Governing equations

− Discretization

− Conceptualization

− Sub-optimal model parameters

− Initial conditions

− Temporal forcing terms

Difficult to quantify

• River model as stochastic process

Stochastic setting

ek The model errors of the system

Updating scheme Measurements included

K Kalman gain matrix Depends on the covariance matrix of state variables

Weighting matrix for the system

The evaluation of the covariance matrix is the main bottleneck

),(1 kkkk εuxx F

KΔxx 11 k

updated

Ensemble Kalman filter

x1k x2

x2k+1 xMk+1x1

Construct

Filter

x1k+1 x2

k+1 xMk+1

Next time step

Simulation

assimilation

Ensemble size M

Output

Measurements

Model errors

Model refinement using an auto regressive process of

first order for model errors

I.e. Updating can be carried out on the model

Test example

L = 23000 mI = 0.025 %

L = 11000 mI = 0.025 %

Q/H relation

Effectiveness of updating algorithm

Internal point

+ reference runooerroneous runD updated

Downstream boundary internal point

+ reference runerroneous run

D updated

Upstream boundary condition updating

+ reference runerroneous run

D updated

Uncertainty assessment

Method uses an ensemble of simulation thus as an added bonus this ensemble may be used to estimate

• Confidence intervals (50% , 80 % etc.)• Standard deviations (in all points)

Valuable tool for sensitivity analysis of boundary conditions

Hardware

Brute force for speed

Parallelization – A case study

• Christchurch, New Zealand Catchment area approx. 420 km2

including three river systems in

the model domain:

Avon River

Styx River

Heathcote River

2D model domain:

4.2 million elements

10 m x 10 m resolution flexible

mesh (rectangular elements)

Distributed rainfall-runoff with

no losses (rain-on-grid)

- 1% AEP event

- 21 hour storm

Hybrid Parallelization – A case study

• Christchurch, New Zealand Run time on desktop PC (MPI)

is 8.9 hours:

16 core Dell Workstation

2 x Intel® Xeon® CPU ES-

2687W v2 (8 core, 3.40 GHZ)

32 GB of RAM

Windows 7 operating system

Run time with 1 x GeForce GTX

TITAN GPU card is 3.1 hours

Run time with 2 x GeForce GTX

TITAN GPU card is 1.7 hours

Hybrid Parallelization• Christchurch, New Zealand

1024; 0.17768; 0.23

512; 0.26

256; 0.45

128; 0.81

64; 1.31

0 200 400 600 800 1000 1200

Number of HPC Cores

Christchurch MIKE 21 FM modelHPC Cluster simulation perfomance

(Simulations executed by HPC Wales)

Below 10 minutes

Klimaspring – next generation smartsolution

“Smart real-time control

of water systems ”

(Smart realtidsstyring af

vandsystemer)

Klimaspring Scope

• Scope:

− Developing an scalable IT-supported system for the real-time monitoring, modelling, warning and management of rainwater in both drainage systems and on the ground.

• Aim:

− Reduce the need to invest in enlarging and upgrading the existing drainage system

− Make managing rainfall less expensive.

− Open up new possibilities for the use of water on the ground

Danish R&D project financed by RealDania

• Collaboration project with university, utility and DHI

Improved rainfall forcasting and

handling of forcast uncertainties

Knowledge of the amount of

rainfall provides the basis of

improving the system

beforehand

Development of computer

models for calculation of where

and how much rainfall you will

have in the system

Possibility of calculating the best

optimization of the system

Real time control of controllable

elements in the sewer system

When rainfall starts the system

can automatically lead the water

to places where it will cause

least damage

User surfaces for display of

operation and system status

Management, planning and

operational personnel can see

how the system is running or

look into control of previous

rainfall events

The system will be able to send

information to e.g. web/mobile

(awaiting final clarification)

Information to citizens

Klimaspring system architecture

• MIKE Powered by DHI software

Key DHI tasks in Klimaspring

• Model engine optimization

− Deterministic models Surrogate models

• Radar data processing

− Improve data image correction and forecasting

• Control algoritm optimization

− Standardaizing and modulazation

• Integration of data and models

• Visualisation

Web front-end

Surrogate models

Model Predictive Control

Why think of a new optimisation approach?

Current optimisation system

• Uses simplified optimisation model (few decision variables)

• Includes execution of many hundreds of simulations

• Takes time

New optimisation system

• Uses detailed optimisation model (thousands of decision variables)

• Model dynamics described by a simplified (surrogate) model

• Takes few minutes on today’s laptop

Local control

System-wide Model Predictive Control (MPC)

Optimisation

Model prediction

Forecast data

Real-time

Implement optimal control

until next optimisation

TimeNext TOF

Optimisation

Model prediction

Updated real-time

and forecast data

assimilation

Operational

Workflow

Optimisation/control problem

Physical system

HiFi model (MIKE model)

Optimiser

Mathematical formulation of (simplified)

optimisation model

Optimisation/control problem

Physical system

HiFi model (MIKE model)

Optimiser

Mathematical formulation of

optimisation model

Surrogate model (Linear)

MPC optimisation framework

What is a surrogate model?

• Derived from the HiFi model (MIKE model)

• Sufficiently accurate for modelling the most important characteristics relevant for the

problem at hand

• Computationally fast

Real-time Control Framework

Optimal combination of HiFi and surrogate models

MPC optimisation with surrogate model

Implementation of

optimal control

Update surrogate model

from HiFi model

MPC optimisation with surrogate model

HiFi model simulation

with implemented

control and assimilation

of system observations

Handling uncertainties

• Uncertainty in model forcing is

described by an ensemble

forecast

• MPC model extended to use

ensemble model forcing (Multiple

• Probability assigned to each

ensemble member

• Will provide optimal control that is

robust to forecast uncertainty

The steps

Surrogate

model1

Formulation of optimisation model• Constraints

• Operation targets

• Objective function

3Behind the scenes• Automatic setup of MPC optimisation model

• Efficient optimisation solver

CARM, Australia

Optimisation of irrigation system

Irrigation Water Delivery Infrastructure

CARM operational goals

• Supply ordered water to the users

The right amount at the right time

• Keep the river in a lean state

Minimize losses due to evapotranspiration

Leave room for accommodating natural inflow

• Keep environmental flow requirements at end-of-system

• Minimise surplus flows at end-of-system

Benefits

• New technology enables solving large system-wide optimisation and control problems

in real-time

− Problems that we cannot solve today without brute force

• May be applied within several business areas

Reduced flooding Environmental

protectionOptimized hydropower

Conclusions

• Forecast system must adapt to roles

• Openness – that suits you

• Smart water solutions

Thank youJohan Hartnack, DHIjnh@dhigroup.com

DHI Blank PowerPoint Presentation UK content/presences/emea/uk... · © DHI #4 Needs •Overview...

Documents