This repo aims at tackling the challenge launched by Enefit in the Kaggle competition Enefit - Predict Energy Behavior of Prosumers. The goal of the competition is to predict the amount of electricity produced and consumed by Estonian energy customers who have installed solar panels. The data set contains consumption, production and weather data, the relevant energy prices, and records of the installed photovoltaic capacity.
predict-energy-behavior-of-prosumers-with-machine-and-deep-learning's Introduction
predict-energy-behavior-of-prosumers-with-machine-and-deep-learning's People
predict-energy-behavior-of-prosumers-with-machine-and-deep-learning's Issues
Make experiments with mlflow
Replace `building_id` by `unit_id`
deploy model using 48 lags
HPO using Random Search
Forecast with RegressorChain
Probabilistic forecasting with MLForecast
Extract features with `tsfresh`
Deploy pipeline with MLForecast
Deploy a model built with MLForecast
Baseline with SageMaker Autopilot
New Random Search
Try different rescaling methods
- StandardScaler
- MinMaxScaler
Train and deploy DeepAR on SageMaker
add Imputer in the pipeline
Hyperparameter optimization
Baseline with AutoGluon
Deploy a simple model for testing the API call
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