Savona, April 10-11, 2014 Task 2.2 Weather Forecasting Data Capturing Module

WP2 Tasks Description WP2 – Data Sources and Architecture of OPTIMUS DSS WP Leader: POLITO Goals: design of overall architecture and features of the OPTIMUS DSS; definition of data sources of the respective software modules for data capturing and modelling. Timing: M6 to M13

DSS Architecture

T2.2 – Design and development of weather forecasting data capturing module Task Leader: D’Appolonia Description: Detailed forecasting of upcoming weather conditions in the target Cities. weather forecasting platforms Assessment of the most reliable weather forecasting platforms. weather parameters Identification of most relevant weather parameters affecting energy demand and consumption. Design of the module based on:  cross check between the sources, spotting the most likely weather conditions with the highest degree of accuracy with a short time resolution (10 mins to 3 hours); historical trends  a database of historical trends for comparison with real time weather forecasting;  daily updates recording the actual weather data; models of energy  possibility to make accurate models of energy demand and of the consequent behaviors of the energy consumers for the coming days.

WP2 Tasks Description Goals: identification of the most suitable and reliable weather forecasting data sources; definition of the specifications for the capturing module serving the DSS; contribution to perform accurate models of energy demand and renewable energy production, aiming to optimize energy use and production. Timing: M9 to M13 Deliverables: D2.2 Weather forecasting data capturing module, due at M13.

Concept Scheme Real time weather forecasting Model processing (Cross check between real time forecast and observed data) Renewable energy production forecast Energy demand model (load profiles) Suggested actions Historical trends database INPUTINPUT INPUTINPUT OUTPUTOUTPUT OUTPUTOUTPUT Weather forecasting service providers Forecasted variables (p, T, etc.) Meteorological observations (p, T, etc.) Mean corrected values of weather variables Energy production optimization Building energy use optimization Central DSS

Required features of Weather Forecasting Model Now-casting (3-4 hours ahead) Short-term forecasting (up to 7 days ahead) Short-term forecasting (up to 7 days ahead) High resolution forecast (pilot level) European context (Netherlands, Spain and Italy) European context (Netherlands, Spain and Italy) Frequent update (e.g. 15 minutes, hour by hour) Frequent update (e.g. 15 minutes, hour by hour) High degree of accuracy Temporal resolution Spatial resolution Geographical coverage Forecasting update Forecasting accuracy

Assessment of the most reliable weather forecasting service providers 1. F ree online sources of weather forecast data (Datameteo Now, Weather Research and Forecasting Model, Euro Weather, etc.) 2. O nline fee-based weather data providers (Ilmeteo.it, Meteogroup, AccuWeather, Renewable Energy Forecasting Service, etc.) 3. Meterological Research Centers (ECMWF, NCEP, NOAA) Service Providers Free Online Fee- based Research Centers Cross check between various weather forecast sources Identification of the most reliable sources

Main correlations between weather variables and energy parameters

Most common formats of weather forecast data Real time weather forecasting Model processing (Cross check of real time and observed data) Historical trends database INPUTINPUT INPUTINPUT OUTPUTOUTPUT OUTPUTOUTPUT Weather forecasting service providers Forecasted variables (p, T, etc.) Meteorological observations (p, T, etc.) Mean corrected values of weather variables The most common formats are: XML; JSON; CSV; PDF; TXT; API. The most common formats are: XML; JSON; CSV; PDF; TXT; API.

Thank you for your attention Any questions?