OPTIMUS DSSoptimus-smartcity.eu/sites/default/files/OPTIMUS DSS_Action Plans_1... · OPTIMUS DSS. Overall Procedure. ... - Resistance-capacitance model For each building, ... with

This project is co-funded

by the European Union

Wednesday, July 6, 2016

Savona, Italy

OPTIMUS DSS

Overall Procedure

OPTIMUS DSS GOAL

How the OPTIMUS DSS works

OPTIMUS DSS setup

DSS graphical user interfaces

Actions plans

Contents

OPTIMUS DSS GOAL

The goal of the OPTIMUS project is to help local authorities to

optimise the energy performance of public buildings by

applying the short-term actions suggested by a Decision SupportSystem (DSS) which handles data obtained in a diversity of sources and

domains:

- Weather conditions- Social behaviour- Building energy performance- Energy prices- Renewable energy production

How the OPTIMUS DSS works (1/8)

Weather forecast

Occupants

feedback

Energy prices

De-centralized

sensor (BEMS)

RES production

Available data

Data is captured from the buildings and their context. Semantic framework

integrates the different data sources using semantic web technologies.


Weather forecast

Occupants

feedback

Energy prices

De-centralized

sensor (BEMS)

RES production

Sunday Monday Tuesday Wednesday Thursday Friday Saturday…Historical data

Pre

dic

tio

n m

od

els

Available data

Prediction models use historical data to forecast

the building behaviour for the following 7 days.


Weather forecast

Occupants

feedback

Energy prices

De-centralized

sensor (BEMS)

RES production

Sunday Monday Tuesday Wednesday Thursday Friday Saturday…Historical data

Pre

dic

tio

n m

od

els

Inference rules

Available data

Inference rules use the predicted and monitored data

to suggest short-term actions plans to the final user.


Weather forecast

Occupants

feedback

Energy prices

De-centralized

sensor (BEMS)

RES production

Sunday Monday Tuesday Wednesday Thursday Friday Saturday…

Pre

dic

tio

n m

od

els

Historical data

En

erg

y M

od

els

Inference rules

• Raise set point temperature

• Shift loads at 11 am

• Partial free cooling at 16 am

• Start heating system at 7 am

Available data

Short-terms actions plans

are presented to the user in

a simple and clear manner.


Weather forecast

Occupants

feedback

Energy prices

De-centralized

sensor (BEMS)

RES production


Pre

dic

tio

n m

od

els

Historical data

Acti

on

pla

ns

sug

gest

ed

by t

he D

SS

Inference rules





Available data

End-users interfaces display the monitored, forecasted data

and the short-term plans in order to support experts’ decisions.

OPTIMUS DSS INTERFACES


Weather forecast

Occupants

feedback

Energy prices

De-centralized

sensor (BEMS)

RES production


Pre

dic

tio

n m

od

els

Historical data

Acti

on

pla

ns

sug

gest

ed

by t

he D

SS

Inference rules





Available data

OPTIMUS DSS INTERFACES

The results of the implementation of the actions

in each pilot city will modify the data sources

Models implementation:

- Multiple linear regressors

- Resistance-capacitance model

For each building, an individual configuration is required

in order to boost the forecasting performance.

Four different prediction modelshave been developed and operatewithin the OPTIMUS DSS toimplement the Inference Rules andsuggest Action Plans.

Weather

forecasting

De-centralized

sensor-based

Feedback from

occupants

Energy prices

RES production


Generation and

On-site renewable

production


AP 1 Scheduling and management of the occupancy

AP 2 Scheduling the set-point temperature

AP 3 Scheduling the ON/OFF of the heating system

AP 4 Management of the air side economizer

AP 5 Scheduling the photovoltaic (PV) maintenance

AP 6 Scheduling the sale/consumption of the electricity

produced through the PV system

AP 7 Scheduling the operation of heating and electricity

systems towards energy cost optimization

Heating and cooling

Occupancy

Air conditioning

DSS server

Savona

Savona SchoolSavona DSS

OPTIMUS DSS Setup (1/5)

Monitoring data

Action plans

- Building Energy Management System

- Monitoring system

- Climate conditions

- Occupants feedback

Data capturing

modules

Savona Campus

Monitoring data

Action plans

Data capturing

modules

- Energy Market

- Renewable energy production system

- CHP, batteries use, etc.

1) Registering a new building

with some static data



2) Setting up the building partitioning

The zones is a logic model of the reality of the

building. Each zone refers to an area of the

building which is monitored (through sensors)

and/or an area where an action plan can be

applied.


3) Setting up the sensors

- Name

- URL service: Web service URL

(RapidAnalytics) of the Prediction model

used to forecast data.

- URL: internal identifier of the sensor,

Prediction model parameters: List of

sensors needed

- Units

- Aggregation method

4 Setting up the action plans

The action plans can be invoked for a

particular zone (previously defined in

step 2).

For each zone where the action plan

will be applied, the sensors needed by

the action plan have to be mapped to

the available sensors of the building

(Previously defined in step 3).


End-user environment: Tracker

DSS graphical user interfaces (1/10)

End-user environment: City Dashboard


End-user environment: Buildings


End-user environment: Building Dashboard


End-user environment: Action Plans


End-user environment: Action Plans


End-user environment: Historical Data


End-user environment: Weekly Report






End-user environment: User Activity


Indicators to be optimized

Energy consumption

CO2 emissions

Thermal comfort

General purpose

Reduction of the building energy consumption by changing the location of

building occupants, so as to use the minimum number of thermal zones

and turn off the heating/cooling system in the empty zones.

By displacing the building occupants to occupy firstly the building zones with

the minor energy consumption.

How does it work?

Action Plans (1/22)

Action Plan 1: Scheduling and management of the occupancy

Structure of the Action Plan

DSS Application

• Occupancy: occupation intensity,

presence time, constraints related to

occupancy

• Thermal needs

Static data

Theoretical Inference rules

Energy Model

Zaanstad

Action Plans (2/22)


Action Plans (3/22)


DSS interface

Conditioned

rooms

Building

zones

unconditioned

rooms


Energy consumption

CO2 emissions

Thermal comfort

General purpose

Optimizing energy use for heating and cooling, while maintaining comfort

levels in accepted ranges.

By supporting the energy manager in adjusting the temperature set-point

after taking into consideration thermal comfort parameters. The preferred

temperature is calculated through the Thermal Comfort Validation (TCV)

and/or the Adaptive Comfort Concept.

How does it work?

Action Plans (4/22)

Action Plan 2: Scheduling the set-point temperature

ISO 7730:2006 & “A framework for integrating User Experience in Action Plan Evaluation through Social Media”. Proceedings

of the 6th International Conference on Information, Intelligence, Systems and Applications (IISA 2015), July 6-8, 2015 - Corfu,

Greece.

Indoor

conditions

Analysis and evaluation

of user’s feedback

Set points

Calculation of the

Predicted Mean Vote

(PMV)

Reconsider

set point temperature…

Predicted

Values

Actual

Values Thermal

Sensation

Building’s

users DeviationInference

Rules

http://validator.optimus-smartcity.eu

Action Plans (5/22)


http://validator.optimus-smartcity.eu/

Outdoor air

temperatureFeedback from

occupants

DSS Application

Sant Cugat

Historical data


Energy Model Predicted data

Zaanstad

Indoor set point

temperature

Savona

Action Plans (6/22)


Structure of the

Action Plan

Action Plans (7/22)


DSS interface

Set point temperature

suggestionBuilding

zones


Energy consumption

CO2 emissions

Thermal comfort

General purpose

Reduction of energy use by optimizing the boost time of the heating

system.

The boost heating phase duration is calculated based on climatic data

forecasts and the occupancy of the building. The social feedback and the

thermal comfort level is also considered.

How does it work?

Action Plans (8/22)

Action Plan 3: Scheduling the ON/OFF of the heating system

Outdoor air

temperature

Indoor air temperature

Energy consumption

Social media/

mining


DSS Application

Sant Cugat

Savona

• On/off of the heating/cooling

system

• Occupied/unoccupied space

• Space heating capacity

Static data

Historical data


Energy Model

Zaanstad

Predicted data

Prediction model

Action Plans (9/22)


Structure of the Action Plan

Action Plans (10/22)


DSS interface

7:00 18:00

6:30 18:00

7:00 18:00

6:00 18:00

7:00 18:00

7:00 18:00

6:30 18:00

7:00 17:00

6:00 17:00

7:00 18:00

8:00 18:00

6:30 18:00

8:00 18:00

6:00 18:00

7:00 18:00

8:00 18:00

6:30 18:00

8:00 17:00

6:00 17:00

7:00 18:00

7:00 18:00

6:30 18:00

7:00 18:00

6:00 18:00

7:00 18:00

Start and stop schedule

of the heating system

Building

zones


Energy consumption

CO2 emissions

Thermal comfort

General purpose

When there is a need for cooling and if the outdoor-air conditions are

favorable, outdoor-air is used to meet all of the cooling energy needs or

supplement mechanical cooling.

How does it work?

Optimizing energy use and reducing energy cost by exploiting outdoor-air to

reduce or eliminate the need for mechanical cooling.


Action Plan 4: Management of air side economizer

Outdoor air temperature

Outdoor relative humidity


Indoor relative

humidity

DSS Application

Sant Cugat

Historical data


Energy Model

Predicted data



Structure of the

Action Plan



DSS interface

Suggestions for doing

free coolingTime schedule of

the suggestions


Energy consumption

Renewable energy production

CO2 emissions

General purpose

By detecting the need for maintenance of the PV system and providing an

alert prompting for appropriate maintenance actions. The identification of

abnormalities and possible problems is facilitated through appropriate

statistical methods.

How does it work?

Optimizing renewable energy production by detecting on time possible

faults of the PV system.


Action Plan 5: Scheduling the PV Maintenance

Weather

conditionsRES Production

Historical data


Energy ModelDSS Application

Sant Cugat

Savona



Structure of the

Action Plan

Predicted data



DSS interface

Alarm status of

the PV system


General purpose

By optimizing the selling/self-consumption of the electricity produced by a PV

system. Different scenarios of energy market are considered (green strategy,

finance strategy, peak strategy).

How does it work?

Optimizing energy consumption or energy cost by exploiting RES production

and load shifting techniques. Maximization of the self-consumption of

electricity produced on-site, and selling of the surplus to make a profit.

Income from the sale of surplus of energy produced through PV system

Energy consumption

Renewable energy production

CO2 emissions


Action Plan 6: Scheduling the sale/consumption of the electricity produced

through the PV system

Weather

forecastingEnergy prices


Energy ModelDSS Application

Sant Cugat

Savona

RES

production

Predicted data Prediction model

RES production




Structure of the

Action Plan




DSS interface

Energy consumption,

production and price

Suggestions to buy energy,

sell energy and shift loads


General purpose

The real use of the infrastructures related with energy consumption and

generation (PV fields, CHP systems and electricity storage) is simulated to

specify based on the season (winter/summer) the schedule of the

heating/cooling systems and then suggestions are made regarding when

the energy generated by the systems of the buildings should be used,

stored or sold in order to minimize energy cost or even make a surplus.

In case that load shifting is possible, additional suggestions are made

regarding when energy intensive processes should be scheduled.

How does it work?

Minimize total energy cost of a building (or block of buildings) by optimizing

simultaneously the operating schedule of its heating and electricity systems.

Energy cost


Action Plan 7: Scheduling the operation of heating and electricity systems

towards energy cost optimization

Weather

forecastingEnergy prices


Energy Model

Structure of the

Action Plan

DSS Application

Savona

RES production

Prediction model

RES production

De-centralized

sensor based

Electricity demand

Thermal demand

Energy prices




DSS interface




Suggestions for load

shifting

Suggestions for optimizing

battery use

Suggestions for optimizing the

operation of the thermal systems

DSS interface




Suggestions for optimizing

battery use

This project is co-funded

by the European Union

Documents

OPTIMUS DSSoptimus-smartcity.eu/sites/default/files/OPTIMUS DSS_Action Plans_1... · OPTIMUS DSS. Overall Procedure. ... - Resistance-capacitance model For each building, ... with