1. OPTIMUS DSS

2. Overall Procedure

3. Contents OPTIMUS DSS GOAL How the OPTIMUS DSS works OPTIMUS DSS setup
DSS graphical user interfaces
Actions plans

4. 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 Support System (DSS) which handles data obtained in a diversity of sources and domains:
Weather conditions
Social behaviour
Building energy performance
Energy prices
Renewable energy production

5. De-centralized sensor (BEMS)
How the OPTIMUS DSS works (1/8)
Available data
Weather forecast
De-centralized sensor (BEMS)
Occupants feedback
Energy prices
RES production
Data is captured from the buildings and their context. Semantic framework integrates the different data sources using semantic web technologies.

6. De-centralized sensor (BEMS)
How the OPTIMUS DSS works (2/8)
Available data
Historical data …
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Weather forecast
De-centralized sensor (BEMS)
Occupants feedback
Prediction models
Energy prices
RES production
Prediction models use historical data to forecast the building behaviour for the following 7 days.

7. De-centralized sensor (BEMS)
How the OPTIMUS DSS works (3/8)
Available data
Historical data …
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Weather forecast
De-centralized sensor (BEMS)
Occupants feedback
Prediction models
Inference rules
Energy prices
RES production
Inference rules use the predicted and monitored data to suggest short-term actions plans to the final user.

8. De-centralized sensor (BEMS)
How the OPTIMUS DSS works (4/8)
Available data
Historical data …
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Weather forecast
Raise set point temperature
Shift loads at 11 am
Partial free cooling at 16 am
Start heating system at 7 am
De-centralized sensor (BEMS)
Occupants feedback
Prediction models
Energy Models
Inference rules
Energy prices
RES production
Short-terms actions plans are presented to the user in a simple and clear manner.

9. OPTIMUS DSS INTERFACES Action plans suggested by the DSS
How the OPTIMUS DSS works (5/8)
Available data
Historical data …
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Weather forecast
Raise set point temperature
Shift loads at 11 am
Partial free cooling at 16 am
Start heating system at 7 am
De-centralized sensor (BEMS)
Occupants feedback
OPTIMUS DSS INTERFACES
Prediction models
Action plans suggested by the DSS
Inference rules
Energy prices
RES production
End-users interfaces display the monitored, forecasted data and the short-term plans in order to support experts’ decisions.

10. OPTIMUS DSS INTERFACES Action plans suggested by the DSS
How the OPTIMUS DSS works (6/8)
Available data
Historical data …
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Weather forecast
Raise set point temperature
Shift loads at 11 am
Partial free cooling at 16 am
Start heating system at 7 am
De-centralized sensor (BEMS)
Occupants feedback
OPTIMUS DSS INTERFACES
Prediction models
Action plans suggested by the DSS
Inference rules
Energy prices
RES production
The results of the implementation of the actions in each pilot city will modify the data sources

11. De-centralized sensor-based Feedback from occupants
How the OPTIMUS DSS works (7/8)
Four different prediction models have been developed and operate within the OPTIMUS DSS to implement the Inference Rules and suggest Action Plans.
Weather forecasting
RES production
De-centralized sensor-based
Models implementation:
- Multiple linear regressors
- Resistance-capacitance model
Energy prices
Feedback from occupants
For each building, an individual configuration is required in order to boost the forecasting performance.

12. How the OPTIMUS DSS works (8/8)
Occupancy
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
Air conditioning
Generation and
on-site renewable
production

13. OPTIMUS DSS Setup (1/5) Buildings DSS server
Data capturing modules
Monitoring data
Action plans
DSS
Monitoring data
Action plans
- Building Energy Management System
DSS server
- Monitoring system
- Climate conditions
- Energy market
- Renewable energy production system
- Occupants feedback

14. OPTIMUS DSS Setup (2/5)
1) Registering a new building with some static data

15. OPTIMUS DSS Setup (3/5) 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.

16. OPTIMUS DSS Setup (4/5) 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

17. OPTIMUS DSS Setup (5/5) 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).

18. DSS graphical user interfaces (1/10)
End-user environment: Tracker
The user’s target for each DSS indicator
The potential for improvement
The improvement so far

19. DSS graphical user interfaces (2/10)
End-user environment: City Dashboard
Mouse hover to see detailed values
The city’s DSS indicators for the last week

20. DSS graphical user interfaces (3/10)
End-user environment: Buildings
The buildings currently registered on the DSS and the four indicators

21. DSS graphical user interfaces (4/10)
End-user environment: Building Dashboard
Mouse hover to see detailed values
The building’s DSS indicators for the last week

22. Unknown action plan status
DSS graphical user interfaces (5/10)
End-user environment: Action Plans
The active action plans for the selected building. Click to see each action plan’s page
Unknown action plan status
Action plan declined
Action plan accepted

23. Choose the week to be displayed
DSS graphical user interfaces (6/10)
End-user environment: Historical Data
Choose the week to be displayed
Click to view
The building’s DSS indicators for the selected week
Plots with the historical or predicted data of the building’s streams

24. DSS graphical user interfaces (7/10)
End-user environment: Weekly Report
Click to edit
The building’s weekly reports

25. DSS graphical user interfaces (8/10)
End-user environment: Weekly Report
User input for the DSS implementation.

26. DSS graphical user interfaces (9/10)
End-user environment: Weekly Report
The action plan status for each day of the week
User input for the action plan implementation

27. DSS graphical user interfaces (10/10)
End-user environment: User Activity
The recent activity of the DSS users

28. Action Plans (1/29)
Action Plan 1: Scheduling and management of the occupancy
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.
How does it work?
By displacing the building occupants to occupy firstly the building zones with the minor energy consumption and secondarily the zones characterized by higher thermal comfort.
Indicators to be optimized
Energy consumption
CO2 emissions
Thermal comfort

29. Action Plans (2/29)
Action Plan 1: Scheduling and management of the occupancy
Structure of the Action Plan
Occupancy: occupation intensity, presence time, constraints related to occupancy
Thermal needs
Outdoor air temperature
Total solar radiation
Indoor air temperature
Energy consumption
Prediction model
Historical data
Static data
Energy consumption
Predicted data
DSS Application
Energy Model
Theoretical Inference rules
Zaanstad

30. Action Plans (3/29)
Action Plan 1: Scheduling and management of the occupancy
Proposed number of occupants
Proposed set-point temperature
DSS interface
Conditioned rooms
Building zones
“Yes” if the zone has to be open
unconditioned rooms

31. Action Plans (4/29)
Action Plan 2: Scheduling the set-point temperature
General purpose
Optimizing energy use for heating and cooling, while maintaining comfort levels in accepted ranges.
How does it work?
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.
Indicators to be optimized
Energy consumption
CO2 emissions
Energy cost

32. Calculation of the Predicted Mean Vote (PMV) set point temperature…
Action Plans (5/29)
Action Plan 2: Scheduling the set-point temperature
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
Deviation
Inference Rules
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, Corfu, Greece.

33. Action Plans (6/29)
Action Plan 2: Scheduling the set-point temperature
Historical data
Structure of the Action Plan
Feedback from occupants
Outdoor air temperature
Indoor set point temperature
DSS Application
Energy Model
Predicted data
Theoretical Inference rules
Sant Cugat
Savona
Zaanstad

34. Set point temperature suggestion Building zones. Click for details
Action Plans (7/29)
Action Plan 2: Scheduling the set-point temperature
DSS interface
Set point temperature suggestion
Building zones. Click for details

35. Action Plans (8/29)
Action Plan 2: Scheduling the set-point temperature
DSS interface
Selected rule and final suggestion
TCV rule suggestion
Click to see the user’s feedback
Adaptive Comfort rule suggestion

36. Hourly feedback from users of the building
Action Plans (9/29)
Action Plan 2: Scheduling the set-point temperature
DSS interface
Hourly feedback from users of the building
The colour code used

37. Action Plans (10/29)
Action Plan 3: Scheduling the ON/OFF of the heating system
General purpose
Reduction of energy use by optimizing the boost time of the heating/cooling system.
How does it work?
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.
Indicators to be optimized
Energy consumption
CO2 emissions
Energy cost

38. Action Plans (11/29)
Action Plan 3: Scheduling the ON/OFF of the heating system
Structure of the Action Plan
On/off of the heating/cooling system
Occupied/unoccupied space
Space heating capacity
Outdoor air temperature
Indoor air temperature
Energy consumption
Social media/ mining
Prediction model
Static data
Indoor air temperature
Historical data
DSS Application
Predicted data
Energy Model
Sant Cugat
Theoretical Inference rules
Savona
Zaanstad

39. Action Plans (12/29)
Action Plan 3: Scheduling the ON/OFF of the heating system
DSS interface
Building zones
Start and stop schedule of the heating system
Proposed set-point temperature
7:00
18:00
7:00
18:00
8:00
18:00
8:00
18:00
7:00
18:00
6:30
18:00
6:30
18:00
6:30
18:00
6:30
18:00
6:30
18:00
7:00
18:00
7:00
17:00
8:00
18:00
8:00
17:00
7:00
18:00
6:00
18:00
6:00
17:00
6:00
18:00
6:00
17:00
6:00
18:00
7:00
18:00
7:00
18:00
7:00
18:00
7:00
18:00
7:00
18:00

40. Action Plans (13/29) Action Plan 4: Management of air side economizer
General purpose
Optimizing energy use and reducing energy cost by exploiting outdoor-air to reduce or eliminate the need for mechanical cooling.
How does it work?
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.
Indicators to be optimized
Energy consumption
CO2 emissions
Energy cost

41. Action Plans (14/29) Action Plan 4: Management of air side economizer
Structure of the Action Plan
Outdoor air temperature
Outdoor relative humidity
Indoor air temperature
Indoor elative humidity
Energy prices
Historical data
Predicted data
Energy Model
Theoretical Inference rules
DSS Application
Sant Cugat

42. Action Plans (15/29) Action Plan 4: Management of air side economizer
DSS interface
Update suggestion base on new parameters
Parameters to be changed by the user
Select calculation method
Suggestions for doing free cooling
Time schedule of the suggestions

43. Action Plans (16/29) Action Plan 5: Scheduling the PV Maintenance
General purpose
Optimizing renewable energy production by detecting on time possible faults of the PV system.
How does it work?
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.
Indicators to be optimized
Energy consumption
Renewable energy production
CO2 emissions

44. Theoretical Inference rules
Action Plans (17/29)
Action Plan 5: Scheduling the PV Maintenance
Structure of the Action Plan
Weather conditions
RES Production
Historical data
Predicted data
Energy Model
DSS Application
Theoretical Inference rules
Sant Cugat
Savona

45. Action Plans (18/29) Action Plan 5: Scheduling the PV Maintenance
DSS interface
Total historical and predicted production
Click for details
Alarm status of the PV system

46. Hourly differences between historical and predicted production
Action Plans (19/29)
Action Plan 5: Scheduling the PV Maintenance
DSS interface
Hourly differences between historical and predicted production
Hour alert

47. Action Plans (20/29)
Action Plan 6: Scheduling the sale/consumption of the electricity produced through the PV system
General purpose
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.
How does it work?
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).
Indicators to be optimized
Income from the sale of surplus of energy produced through PV system
Energy consumption
Renewable energy production
CO2 emissions

48. Theoretical Inference rules
Action Plans (21/29)
Action Plan 6: Scheduling the sale/consumption of the electricity produced through the PV system
Structure of the Action Plan
Weather forecasting
Energy prices
RES production
Predicted data
Prediction model
RES production
DSS Application
Energy Model
Theoretical Inference rules
Sant Cugat
Savona

49. Select calculation strategy Energy consumption, production and price
Action Plans (22/29)
Action Plan 6: Scheduling the sale/consumption of the electricity produced through the PV system
DSS interface
Select calculation strategy
Energy consumption, production and price

50. Action Plans (23/29)
Action Plan 6: Scheduling the sale/consumption of the electricity produced through the PV system
DSS interface
Suggestions to buy energy, sell energy and shift loads
Click to show/hide graph
Energy consumption, production and price

51. Action Plans (24/29)
Action Plan 7: Scheduling the operation of heating and electricity systems towards energy cost optimization
General purpose
Minimize total energy cost of a building (or block of buildings) by optimizing simultaneously the operating schedule of its heating and electricity systems.
How does it work?
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. 
Indicators to be optimized
Energy cost

52. De-centralized sensor based Theoretical Inference rules
Action Plans (25/29)
Action Plan 7: Scheduling the operation of heating and electricity systems towards energy cost optimization
Structure of the Action Plan
De-centralized sensor based
Weather forecasting
Energy prices
RES production
Prediction model
RES production
Energy Model
Electricity demand
DSS Application
Theoretical Inference rules
Thermal demand
Savona
Energy prices

53. Action Plans (26/29)
Action Plan 7: Scheduling the operation of heating and electricity systems towards energy cost optimization
DSS interface
Suggestions for load shifting
Suggestions for battery use
Suggestions for the operation of the thermal systems
Energy flows. Choose day to display

54. Suggestions for load shifting
Action Plans (27/29)
Action Plan 7: Scheduling the operation of heating and electricity systems towards energy cost optimization
DSS interface
Suggestions for load shifting
Remove load
Add load

55. Suggestions for battery use Discharge the batteries
Action Plans (28/29)
Action Plan 7: Scheduling the operation of heating and electricity systems towards energy cost optimization
DSS interface
Suggestions for battery use
Discharge the batteries
Charge the batteries

56. Action Plans (29/29)
Action Plan 7: Scheduling the operation of heating and electricity systems towards energy cost optimization
DSS interface
Suggestions for the operation of the thermal systems
Energy to be produced by ThA
Energy to be produced by ThB

57. Thank you for your attention!
This project is co-funded by the European Union 57