Day-ahead electricity price forecasts 2 to 9 days out, hourly, in EUR/MWh, for every bidding zone in Denmark, Norway, Sweden, Finland and the Netherlands: Retrained and re-uploaded daily, so the weights here are what production runs. A companion model specialised to Denmark alone lives at A single LightGBM quantile model covers all 13 zones, with zone as a categorical feature. The zones share nearly all their structure — the same calendar effects, the same weather physics, the same lag behaviour — so pooling lets a thin zone borrow strength from a busy one, and leaves one thing to maintain instead of thirteen. Pooling only works because the model can identify a zone's price level before it…
Open weights
cc-by-4.0
lightgbm
Sentinel-Mumbai Hybrid PINN is an attention-augmented Physics-Informed Neural Network (PINN) developed in Keras 3 and optimized for high-throughput GPU inference (Tesla T4 mixed-precision float16). The network incorporates a 24-hour temporal sequence lookback and enforces hydraulic mass conservation via a partial differential equation (PDE) continuity constraint during gradient descent: $$\text{PDE Residual} = \frac{\partial \hat{y}}{\partial t} - (\text{Rainfall} - \text{Infiltration}) = 0$$ Where $\text{Infiltration} \approx 0.0$ on impervious urban concrete surfaces (landcover class 80). Released under the MIT License.
Open weights
mit
keras
Sentinel-V7 Deep Hydrological Bi-LSTM is a specialized recurrent neural sequence model engineered for real-time flood risk scoring and inundated area forecasting. It maps multi-sensor remote sensing inputs (Copernicus Sentinel-1 SAR radar backscatter, digital elevation models, river network proximity, ECMWF GEOGloWS river discharge, and precipitation metrics) to a continuous flood vulnerability metric. The model is packaged in Keras 3 format along with its fitted Scikit-Learn MinMaxScaler (scaler.joblib), ensuring seamless deployment on edge systems and cloud microservices. Released under the MIT License.
Open weights
mit
keras
Sentinel-Mumbai PINN v1 is a high-capacity Physics-Informed Neural Network (PINN) engineered for hyper-local urban flood depth forecasting and storm surge dynamics across Mumbai / Salsette Island. Trained across multi-decadal telemetry (rainfall, cumulative precipitation, tidal surge oscillations, elevation, urban impermeability, and drainage networks), the model embeds physical boundary laws directly into its optimization objective, preventing unphysical mass disappearance during coastal hydraulic lock events. The model optimizes a composite objective balancing empirical data fidelity against hydraulic conservation of mass: $$\mathcal{L}{\text{total}} = \alpha \cdot…
Open weights
mit
pytorch
R
Model · Tabular regression
Reddy
A multi-output Random Forest regressor for predicting residential property energy consumption and valuation, built for the Lloyds Green Home Score ESG intelligence platform. This model predicts three targets simultaneously from 13 property features: - Synthetic simulator consistency only, not real-world accuracy - Current value model validation is synthetic, not historical UK transaction validation - Value ranges are sensitivity bands, not calibrated confidence intervals Part of the Lloyds Green Home Score platform for ESG risk assessment, retrofit recommendation, and green lending analytics. Not intended for direct property valuation or energy certificate prediction. - multioutputrf.joblib…
Open weights
mit
sklearn
Mitra regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra regressor, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra regressor: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
Open weights
apache-2.0
76M parameters
