Pinned Repositories
bioacoustic-monitoring
This tutorial presents an "agile modeling" approach that enables users to build custom classifier systems efficiently for species of interest using transfer learning, audio search, and human-in-the-loop active learning.
building-control-boptest
Apply reinforcement learning to a building emulator to intelligently control HVAC systems.
building-load-forecasting
Accurate forecasts of energy demand and supply are essential to mitigate climate change. Discover how to train and evaluate building load forecasts using off-the-shelf ML models.
citylearn
Learn how to design simple and advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment and its datasets for extended use in projects. The tutorial provides a walk-through on how to set up and interact with the environment using a real-world dataset in three hands-on control experiments.
flood-monitoring
Floods in coastal areas can be extremely destructive natural hazards resulting in societal and economical damage. In this tutorial, explore how to predict building and population density to understand the potential impact from a flooding event.
lulc-classification
Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.
nlp-policy-analysis
Explore how Natural Language Processing (NLP) can be used to assist in identifying and mapping climate-relevant literature using a supervised learning approach and leverage a state of the art Large Language Model (LLM) to classify climate policy documents.
optimal-power-flow
AC Optimal Power Flow (OPF) attempts to determine the setpoints of generators that would minimize the operating cost of a power system while meeting other operational constraints. In this tutorial, learn how to leverage PyTorch to train a neural network to approximate the optimal solutions.
tracking-ml-emissions
Learn how to measure a machine learning model's carbon footprint and practice strategies that can help shrink the energy involved in training these models.
zevi
This tutorial surveys different methods to formulate electric vehicle (EV) charging and energy dispatch as an optimization problem, using tools such as convex optimization, Markov decision process (MDP), and reinforcement learning (RL).
climatechange-ai-tutorials's Repositories
climatechange-ai-tutorials/nlp-policy-analysis
Explore how Natural Language Processing (NLP) can be used to assist in identifying and mapping climate-relevant literature using a supervised learning approach and leverage a state of the art Large Language Model (LLM) to classify climate policy documents.
climatechange-ai-tutorials/lulc-classification
Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.
climatechange-ai-tutorials/bioacoustic-monitoring
This tutorial presents an "agile modeling" approach that enables users to build custom classifier systems efficiently for species of interest using transfer learning, audio search, and human-in-the-loop active learning.
climatechange-ai-tutorials/optimal-power-flow
AC Optimal Power Flow (OPF) attempts to determine the setpoints of generators that would minimize the operating cost of a power system while meeting other operational constraints. In this tutorial, learn how to leverage PyTorch to train a neural network to approximate the optimal solutions.
climatechange-ai-tutorials/building-control-boptest
Apply reinforcement learning to a building emulator to intelligently control HVAC systems.
climatechange-ai-tutorials/zevi
This tutorial surveys different methods to formulate electric vehicle (EV) charging and energy dispatch as an optimization problem, using tools such as convex optimization, Markov decision process (MDP), and reinforcement learning (RL).
climatechange-ai-tutorials/coal-power-mrv
Explore how to monitor coal power plant activity by leveraging satellite imagery and computer vision models.
climatechange-ai-tutorials/tracking-ml-emissions
Learn how to measure a machine learning model's carbon footprint and practice strategies that can help shrink the energy involved in training these models.
climatechange-ai-tutorials/building-load-forecasting
Accurate forecasts of energy demand and supply are essential to mitigate climate change. Discover how to train and evaluate building load forecasts using off-the-shelf ML models.
climatechange-ai-tutorials/citylearn
Learn how to design simple and advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment and its datasets for extended use in projects. The tutorial provides a walk-through on how to set up and interact with the environment using a real-world dataset in three hands-on control experiments.
climatechange-ai-tutorials/flood-monitoring
Floods in coastal areas can be extremely destructive natural hazards resulting in societal and economical damage. In this tutorial, explore how to predict building and population density to understand the potential impact from a flooding event.
climatechange-ai-tutorials/open-catalyst-project
Chemical reactions are one approach to convert intermittently available renewable energy, such as solar or wind, to other fuels, like hydrogen. Learn how ML can discover low-cost catalysts to drive these reactions at high rates.
climatechange-ai-tutorials/mobility-demand
Ensuring a shared bike is readily available can reduce demand for less climate-friendly transportation options. Explore how to model the relationship between bike usage and points of interest (POIs) to identify the best locations for new shared-bike stands.
climatechange-ai-tutorials/aquaculture-mapping
Managing aquaculture ponds is vital for environmental monitoring and conservation. This tutorial presents how to leverage satellite imagery and semantic segmentation models to detect and map aquaculture ponds based on production intensity.
climatechange-ai-tutorials/camera-trap-recognition
The greatest logistical barrier to long-term wildlife monitoring with camera traps is the overwhelming amount of human labor needed to annotate thousands or millions of images for ecological analysis. In this tutorial, learn how to train a deep learning model to automatically identify animals in camera trap images.
climatechange-ai-tutorials/climatelearn
Apply machine learning to predict climate variables into the future and transform low-resolution outputs of climate models into high-resolution regional forecasts.
climatechange-ai-tutorials/fourcastnet
Learn how to use FourCastNet, a weather model based on deep learning, to obtain short to medium-range forecasts of crucial atmospheric variables such as surface wind velocities.
climatechange-ai-tutorials/lulc-semantic-segmentation
Learn how to create a custom LULC dataset by combining multispectral satellite and vector data.
climatechange-ai-tutorials/quantus-x-climate
In climate science, explainable artificial intelligence (XAI) can be used to improve and validate deep learning methods, but evaluation and selection of XAI methods is challenging. Learn how to use the explainable AI evaluation package Quantus to compare and select an appropriate XAI for your climate AI research task.
climatechange-ai-tutorials/seasonal-forecasting
Existing El Niño forecasts use dynamical models that rely on the physics of the atmosphere and ocean. Learn how to create El Niño forecasts using machine learning instead, which uses statistical optimization to issue forecasts.
climatechange-ai-tutorials/smart-meter-analytics
A practical guide to current trends in smart meter data analytics with a focus on feature engineering and machine learning scenarios for energy data at 15-minute resolution. Gain insights into current trends and use cases in the energy field and get a sense of typical and atypical energy consumption in a residential building.