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ae_ts

Auto encoder for time series

amazon-sagemaker-byoa-prophet-tutorial

Este tutorial muestra como integrar algoritmos propios a Amazon Sagemaker.

amazon-sagemaker-time-series-prediction-using-gluonts

analytics-zoo

Distributed Tensorflow, Keras, PyTorch and BigDL on Apache Spark

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

antaresflowbased

antaresprocessing

Processes the input and output data of ANTARES

antaresviz

ANTARES Visualizations

australian-national-electricity-market-with-and-without-flexible-ramping-products

Australia's goals of increased penetration of renewable energy such as wind energy will inevitably lead to increased variability and uncertainty of the ramps in net load (load minus non-dispatchable renewable generation). This increased variability and uncertainty requires conventional generators to be more flexible, but currently this flexibility is not fully integrated in market processes. The provision of additional flexibility may cause a reduction in economic efficiency, consumer surplus and/or producer surplus as conventional generators may need to modify their output from the optimal level in order to provide flexibility to account for future variability and uncertainty. As a solution to this problem, the Midwest and Californian Independent System Operators have proposed flexible ramping products as a mechanism to manage the uncertainty and variability in net load ramps in an economically preferable manner. The mechanism essentially aims to schedule conventional generators to provide enough ramping capability, or "flexibility", to satisfy a flexible ramping capability requirement. This requirement is designed to ensure a certain range of ramps in the next interval could be met, whether the ramps actually occur or not. This study aims to explore the implementation of flexible ramping products in the specific context of the Australian National Electricity Market (NEM), to determine whether or not they can be an effective mechanism for integrating variable renewable energy in Australia in the coming decades. This model is a simplified model of the Australian NEM, in which a unit commitment and economic dispatch is designed with flexible ramping products and a flexible ramping requirement. The simplification of the NEM includes a grouping of the five states into two regions, and an aggregation of generators by offered ramping speed and actual marginal costs. Actual load and wind generation data from the 2014/15 financial year is implemented in the model to attempt to simulate the market in a realistic manner.

auto-ml-toy-datasets

A bunch of data sets to evaluate autoML tooling (autogluon, pycaret, h2o, cloud services for autoML etc)

autoencoder_classifier

Autoencoder model for rare event classification

automl_service

Deploy AutoML as a service using Flask

awesome-data-labeling

A curated list of awesome data labeling tools

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

awesome_time_series_in_python

This curated list contains python packages for time series analysis

aws-lambda-r-runtime

Experiments to run R in AWS Lambda

azure-open-ai-embeddings-qna

batchtools

Tools for computation on batch systems

bcgov-rstats-public-presentations

Content (slide decks etc) from presentations related to use of R (#rstats) in the B.C. government

benchm-ml

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

bike_predict

A demo of an end-to-end machine learning pipeline, using RStudio Connect

blog

Collection of the codes which are used on our blog at https://www.statworx.com/de/blog/

bokeh-python-visualization

A Bokeh project developed for learning and teaching Bokeh interactive plotting!

cit_lstm_timeseries

LSTM Model for Electric Load Forecasting

cold-start-energy-forecasting

This project challenging and interesting enough for me. I open to you who wanna join in this project. for having fun guys. money is bonus.

companies-using-r

A Curated list of R uses in entreprise

computational-fluid-dynamics-machine-learning-examples

This repo contains some tutorial type programs showing some basic ways machine learning can be applied to CFD.

connect-forecasting-demo

cortana-intelligence-energy-demand-forecasting

Energy industry solutions using the Cortana Intelligence Suite with end-to-end walkthrough.

coursera

Assignments for Coursera DataScience Specialization