Generod ontology for solar irradiance forecasting

1. TOWARDS A GENERIC ONTOLOGY FOR
SOLAR IRRADIANCE FORECASTING
MASTER’S REPORT
ABHILASH KANTAMNENI

2. Data Source – EIA.gov
Image credit – MiDataLabs.org

3. Solar Markets - Rates, growth and
opportunities
Data Source – SEIA Annual
Report 2015

4. Solar Markets - Rates, growth and
opportunities
Data Source – SEIA Annual
Report 2015

5. Data Source – NEISO
Image credit – MiDataLabs

6. Solar Forecast Users & Applications
MAkhyoun, M. Solar Electric
Power Association Report, 2014
Widiss, R.; Porter, K. NREL
Technical Report, 2014
Zieher, M et. al, German Federal
Ministry for Economic
Cooperation and Development,
2015

7. Solar Irradiance - Basics
Image credit – Kantamneni et al,
ARL Technical Report, 2015
Amrouche, B.; Le Pivert, X.
Applied energy, 2014

8. Forecast Models Overview
Diagne, M.; et al, Renewable and
Sustainable Energy Reviews, 2013

9. Forecast Models Empirical
Angstrom, A. Quarterly Journal of the
Royal Meteorological Society, 1924
American Society of Heating,
Refrigerating and Air Conditioning
Engineers, 1972
Paulescu, M, Modeling Solar
Radiation at the Earths Surface;
Springer, 2008;

10. Forecast Models Radiative Models
GHI Top of Atmosphere
Mass of air
Transmittance
GHI
Ineichen, P. Solar Energy 2008Mueller, R et. al, Remote
Sensing of Environment ,2004
Schmetz. J. et al, Bulletin of the
American Meteorological Society,
2002

11. Forecast Models Sky Imager Model
Chow, Chi Wai, et al. Solar Energy ,
2011
Marquez, R, et al. Solar Energy ,
2013
Ghonima, M. S et al. Atmospheric
Measurement Techniques, 2012
Image credit - Wikimedia

12. Forecast Models NWP Models
Image credit - Wikimedia
Mathisen, P, et al. Solar Energy ,
2011

13. Forecast Models ANN
Weather
Day of Year
Archived Data
GHI
Kalogiru, S. Applied Energy, 2000 Mellit, A. International Journal of
Artificial intelligence and soft
computing, 2008
Yadav, A. Renewable and
Sustainable Energy Reviews,
2014

14. Forecast Models Persistence
GHIt-1 = GHIt

15. Forecast Models Summary
Diagne, M.; et al, Renewable and
Sustainable Energy Reviews, 2013

16. Solar Forecast Metrics Zhang, J. et. al, Solar Energy,
2015
Zhang, J. et. al, 3rd International
workshop on integration of solar
power into power system, 2013.
Marquez, R.; Coimbra, C. F.
Journal of solar energy
engineering 2013
US Department of Energy, Solar
Forecasting Metrics Stakeholder
Webinar, 2014

17. Challenge
s• Communities may lack common understanding
of domain in which they interact
• Poor communication due to lack of shared
vocabulary
• Domain assumptions and requirements
unspecified
• Diverse approaches to structuring and
organizing information

18. Ontologies
• “an explicit and formal specification of a
conceptualization”
• Formally model a system, constituent entities
and relationships
• Reuse knowledge of the domain
• Make domain assumptions explicit
• Separate domain knowledge from operational
knowledge

19. Ontologies
• CYC Ontology - "codify, in machine-usable form,
millions of pieces of knowledge that compose
human common sense”
• Time Ontology - describe temporal content of
Web pages
Examples
Allen, J. F.; Ferguson, G. Spatial
and Temporal Reasoning 1997
Reed SL, et al. AAAI 2002 Conference
Workshop on Ontologies, 2002

20. OWL
• Individuals- objects in the domain of interest
• Properties- describe features and attributes of
individuals
• Classes- sets that contain individuals whose
descriptions precisely describe the class
membership requirements
Web Ontology Language

21. Individuals

22. Properties
• Datatype - relationship between objects and data values
• Annotation - metadata, version information
• Object properties –
• Functional
• Inverse
• Transitive
• Symmetric
• Asymmetric
• Reflexive
• Irreflexive

23. Properties

24. Classes

25. SF-ONT
Introductory ontology mapping
the domain of solar irradiance
forecasting

26. Using Protege Noy, N. et. al, The
Semantic Web, 2001

27. Ontology Development
Methodologies
• Uschold and King
• SENSUS
• Methodontology
• On-To-Knowledge
• Ontology 101

28. Ontology 101
• Determine domain and scope
• Reuse existing ontologies
• Enumerate important items
• Classes and class hierarchy
• Define properties
• Create individuals

29. 1) Specification
• Competency questions
• What is the domain that the ontology will
cover?
• For what we are going to use the ontology?
• For what types of questions the information in
the ontology should provide answers?
• Who will use and maintain the ontology?

30. 1) Specification
• Competency questions

31. 2) Reuse
• Instants
• Datetimestamp, Start time, End time
• Intervals
• Duration, overlap, begins, ends
• Time Zone
OWL Time
URI: https://www.w3.org/TR/2016/WD-owl-time-20160712/

32. 2) Reuse
• Latitude
• Longitude
• Spatial concepts
• Polygon
• Region
• Area
OWL Basic Geo
URI: https://www.w3.org/2003/01/geo/

33. 2) Reuse
• Quantities and units of measurement in science
and engineering
• SI base units
Units of Measurement
Rijgersberg, H.; van Assem, M.;
Semantic Web ,2013

34. 2) Unused
• Weather Station
• Weather ONT
• WeatherOntology
Weather
URI: https://www.w3.org/2005/Incubator/ssn/ssnx/weather-station/station
URI:http://www.scs.ryerson.ca/~bgajdero/msc_thesis/code/ontologies/we
ather-ont-t2.owl
URI:https://www.auto.tuwien.ac.at/downloads/thinkhome/ontology/Weat
herOntology.owl

35. CSP Ontology2) Unused
Source - Piazza, Antonino, and Giuseppe Faso. "Concentrated Solar Power:
Ontologies for Solar Radiation Modeling and Forecasting." Advances onto the
Internet of Things. Springer International Publishing, 2014. 325-337.

36. Hierarchical Taxonomy3)Classes

37. Hierarchical Taxonomy3)Classes

38. Hierarchical Taxonomy3)Classes

39. 4) Properties

40. 4)Properties

41. 4)Properties

42. 4)Properties

43. 5)Using Reasoners
• Check logical consistency

44. 5)Using Reasoners
• Automatic reasoning for inferred hierarchies

45. 5)Using Reasoners
• Asserted
hierarchy

46. 5)Using Reasoners

47. 5)Using Reasoners Inferred hierarchy

48. SF-ONT Summary

49. Use Case #1
Given a forecast model, what
are the appropriate end users
that can use it?

50. Use Case #1

51. Use Case #1
• Inferred
hierarchy

52. Use Case #2
Given access to some data
(input variables), what are
appropriate grid applications?

53. Use Case #2

54. Use Case #2
• Inferred
hierarchy

55. Use Case #3
For specific end users, what
forecasting models should they
be using?

56. Use Case #3

57. Use Case #3
Inferred hierarchy

58. Future Work Instances of each forecast
model

59. Future Work
• Instances of each model
• Smart-grid standards interoperability
• Expand and maintain

60. Questions?

61. Solar Irradiance
Model
Mathematical
Model
Time
Location
Weather
(other)
Irradiance
Forecast
PV Materials
Orientation
Power
Forecast