As global warming intensifies, learning how to adapt to climate changes and consequent extreme weather events is gaining urgency. More accurate weather models and intelligent warning systems enable the improvement of the resilience of the local areas and production activities. One way of achieving this is through obtaining more accurate short term weather forecasts tailored for specific applications by analyzing large amounts of publicly available data such as localized meteorological measurements obtained from IoT sensors, open-source forecasts and even Earth observation data. In this talk we will show how we apply machine learning algorithms to efficiently improve and transform weather forecasts obtained from meteorological services and implement them in various decision-making use-cases such as precision agriculture, heating and cooling in buildings, urban infrastructure optimization (water distribution, urban lighting, traffic), logistics optimization and many more.

1. Laboratory for Renewable Energy Systems - FER
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Laboratory for Renewable Energy Systems
Nowcasting: AI-enhanced short-term weather forecasts for decision-making
https://www.lares.fer.hr

2. Laboratory for Renewable Energy Systems - FER
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Faculty of Electrical Engineering and Computing, Zagreb, Croatia
• 3500 students
• 500 MSc annually
• 700 employees
• 260 projects
• ~400 mil. €
• 50% financing
• Center for AI

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• 40 people (10+ PhD)
• 20 ongoing projects
• Horizon Europe, IRI2, STRIP,
Interreg, HRZZ, EFRD
• ~ 10 mil. €
• 85% researchers
LARES - Laboratory for Renewable Energy Systems

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• We do AI in sustainability.
• We do optimization, design of control systems for real-
time optimal and predictive autonomous decisions of
linear and quadratic program format, and by meta-
heuristic approaches, capturing system models by
physical representation and differential equations
supported by estimation techniques, forecasting
disturbances and uncertainties stochastically, and by
neural networks and machine learning algorithms.
• We apply it to renewable energy systems, smart grids,
smart buildings, city infrastructure, and agriculture.
LARES - Laboratory for Renewable Energy Systems

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Why?
• More accurate and more reliable weather forecast
• Bridge the gap between forecast info and end-users
• Decision support (nowcasting) in smart and precision sectors
(agriculture, urban and energy)
What?
• Localization
• Correction
• Transformation
• Tailoring (applications)
How?
• Multi-modal data sources:
– Wide network of local meteorological stations
– Various forecasting systems and models
– Satellite and radar imagery
– …
• AI algorithms :)
AI-enhanced weather forecasting

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4km x 4 km
Landsat 8 (OLI TIRS
sensor) satellite image of
Land Surface Temperature

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Methodology
• In situ meteorological measurements + weather forecasts
• Model the forecast error and perform forecast corrections
• Time series inputs (lags, rolling windows, date times, …)
• Dynamic propagation (recursive) approach with LGBM,
one-shot (multi-input multi-output) approach with TabNet
• Optuna hyperparameter optimization + time series cross-validation strategy

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Case studies
• LARES (FER skyscraper) meteo station
– Air temperature, pressure and humidity
– Solar irradiance (direct, diffuse, global,
active sun tracking)
– Wind speed and direction
– 1-minute resolution
Scenario 1:
• Aladin-HR forecast (DHMZ)
• LGBM
• Dynamic propagation
• Air temperature and humidity
• Global solar irradiance
Scenario 2:
• OpenWeather forecast
• TabNet
• One-shot approach
• Air temperature and humidity
• Precipitation

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Aladin-HR
• Croatian Meteorological and
Hydrological Service (DHMZ)
• ALADIN-HR model:
– horizontal resolution of 4 km and 2 km
– hourly availability of prognostic products
up to 72 hours in advance
– new forecast calculation every 6 hours
• Special weather forecasts calculated for the FER skyscraper building

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Aladin-HR – approach
• 2 years for training, 8 months testing
• MAE loss and validation metric
• Inherent regularization + early stopping
• Optuna hyperparams tuning
• Time-series CV
• Shorter training / longer inference

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Aladin-HR – results
MAE RMSE R2
1h 24h 1h 24h 1h 24h
Air
temperature
ALADIN 1.63 1.86 2.10 2.46 0.94 0.92
ML 0.64 1.58 0.97 2.11 0.99 0.94
Air
humidity
ALADIN 8.63 9.48 11.09 12.01 0.53 0.45
ML 2.44 6.14 3.49 7.91 0.95 0.75
Global
irradiation
ALADIN 53.61 62.78 104.08 120.75 0.79 0.72
ML 38.70 53.87 83.41 114.60 0.87 0.76
Temperature ALADIN ML
Reliability* 63.84% 71.25%
Inaccuracy** 4.97% 3.47%
* < ±2°C
** > ±5°C

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Aladin-HR – results

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OpenWeather service
• Historical, current and forecasted weather data via APIs
• Different data sources (radars, vast network of weather stations,
data from global/local providers such as NOAA, Environment
Canada and the Met Office)
• CNN/ML models used for enhancing weather forecasting
• 2 days ahead – 1 hour resolution
• Refreshed at every fetching

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OpenWeather service – approach
• 1.5 years for training, 8 months
testing
• MAE loss and validation metric
• Inherent regularization + early
stopping
• Longer training / shorter inference
• More uniform feature importance
Arik, Sercan Ö., and Tomas Pfister. "Tabnet: Attentive interpretable tabular learning."
Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 8. 2021.

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OpenWeather service - results
MAE RMSE R2
1h 24h 1h 24h 1h 24h
Air
temperature
OW 1.26 1.61 1.75 2.11 0.95 0.93
ML 1.04 1.37 1.44 1.78 0.97 0.95
Air
humidity
OW 5.5 8.77 7.44 11.31 0.79 0.51
ML 3.52 5.87 4.79 7.67 0.92 0.76
Total
precipitation
OW 0.11 0.12 0.51 0.53 0.21 0.08
ML 0.06 0.07 0.32 0.39 0.35 0.15
Temperature OW ML
Reliability* 70.52% 76.97%
Inaccuracy** 2.92% 1.59%
* < ±2°C
** > ±5°C

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Key findings
• ML based corrector (not a meteorological model)
• 10% - 60% accuracy improvements
• Recursive approach – nowcasting, one-shot – overall improvement
• Irradiance and precipitation require additional data and different validation strategies (CV and
testing)

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• Dynamic propagation on longer horizons
• Additional meteorological measurements (from the
same location)
• Additional weather forecast variables
(for the same location)
• Merge of multiple weather forecasts
(for the same location)
• Custom loss metrics for one-shot approach
• Multimodal data (satellite/radar imagery)
• Clusters of local weather stations
• Uncertainty estimation
• Ensembles of correctors
• Hybrid models (physical and statistical)
• Forecasts tailoring/transformations
Fails and dead-ends Future work

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Applications

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Smart cities (urban)
• Urban infrastructure:
– Public transportation system (travel times and usage)
– Water distribution and waste water systems
– Power distribution grid (consumption/production)
– City lighting
– Spatial planning and development
• Urban mobility:
– Roadway icing (e.g. on bridges)
– Bike rental usage
• General (public) usage:
– Events and manifestations
– Health (urban heat islands)
• Extreme weather events

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• Holistic approach (thermodynamic building model, control of
heating/cooling system, RES, energy storage system, user behaviour,
localized weather forecast)
• Smart operation planning for 1-7 days ahead
• Reduced operation costs, improved energy efficiency, increased
comfort
• Active cooperation with DSO and the energy market
Smart buildings

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Smart agriculture
• Precision agriculture:
– Irrigation management
– Pest development and treatment
– Frost predictions
– Soil state predictions
• Development predictions
– Yield predictions
– Optimal sowing and reaping times
(scheduling)
• Extreme weather events

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AgroSPARC
• Smart and predictive agriculture for
resilience to climate change
• Prototype growth chambers
• IoT and multispectral cameras
• Big and fast data
• Machine learning models

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Smart agriculture
J. C. Zadoks, T. T. Chang, and C. F. Konzak, “A decimal code for the growth stages of cereals,” Weed Research, vol. 14, no. 6, pp.
415–421, 1974.
• Individual zones microclimate
(temperature, humidity, soil
moisture)
• Multispectral imagery
• ML modelling for:
Plant development prediction
Yield prediction
Prest treatment optimisation
Grain humidity prediction
Harvest scheduling

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Smart grids (energy)
• Consumption forecasting
– Electrical/thermal energy
• Renewable energy production forecasting
– PV and wind power plants
– Clouds movement predictions
– Wind gusts
• Demand response
• Equipment protection and maintenance
• Extreme weather

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MODEL
CALIBRATION
INITIAL
PREDICTION
MODEL
- historical data
- other data (time, historical
weather, etc.)
DATA
- actual data
DATA
+
prediction
prediction
error
PREDICTION
MODEL
- historical data
- other data (time, weather, etc.)
DATA
MODULE INPUTS
Historical weather measurements:
• Temperature
• Direct, diffuse solar irradiance
• Solar zenith and azimuth angles
MODULE
Locally stored
Historical PV production data
PV production ML system
(in operation since June 2018)
MODULE OUTPUTS
Stored
Analyzed
Visualized

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…

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https://www.lares.fer.hr