edgBR

CIT_LSTM_TimeSeries

LSTM Model for Electric Load Forecasting

Energy_Demand_Forecasting

UPC KTH Master Thesis on Energy Demand Forecasting for Smart Buildings. Developed in R with actual buildings data

JuliaBox

power-laws-forecasting

Winners of the Power Laws forecasting competition

power-laws-optimization

Example repository for the Power Laws: Optimizing Demand-side Strategies competition on DrivenData

automl_service

Deploy AutoML as a service using Flask

Bokeh-Python-Visualization

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

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

DA-electricity-price-forecasting

Forecasting Day-Ahead electricity prices in the German bidding zone with deep neural networks.

datasharing

The Leek group guide to data sharing

DeepLearning-time-series

LSTM for time series forecasting

economic_dispatch_pyomo

This is the code to solve a simple economic dispatch model using pyomo

ElectricityDemandForecasting

Electricity demand forecasting for Austin, TX, using a combination of timeseries methods and regression models

Energy-Forecasting-FULL-PIPELINE

Data Scraper, FBProphet, XGBoost

ForecastingElectricityPrices

Thesis project on forecasting german (epex spot) electricity prices

gefcom2017

GEFCom2017-D modelling and forecasts. D stands for defined-data track.

Greek-Electric-Load-Forecasting-IPTO

This repo contains the code for my postgraduate thesis dealing with Short-term Load Forecasting, predicting the electric load demand per hour in Greece, developed in R, RStudio, R-markdown and R-Shiny using daily load datasets provided by the Greek Independent Power Transmission Operator (I.P.T.O.). A presentation of the thesis' results can be found at the following website:

MultiStepAheadForecasting

multi-step ahead forecasting of spatio-temporal data

Optimization-Pyomo

Linear and Nonlinear programing with Pyomo

Phy-Net

compressing physics with neural networks

power-laws-anomalies

predictive-maintenance-python

PyomoGallery

A collection of Pyomo examples

rnn_multistep_ahead_forecasting

Code in Python for my blog post on implementing time series multi-step ahead forecasts using recurrent neural networks in TensorFlow.

stELMOD

stELMOD is a stochastic optimization model to analyze the impact of uncertain wind generation on the dayahead and intraday electricity markets as well as network congestion management. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure resembling the market process of most European markets.

Stochastic-Unit-Commitment

Stochastic Unit Commitment for Renewable Energy Supply using Lagrangian Decomposition

TensorFlow-Time-Series-Examples

Time Series Prediction with tf.contrib.timeseries

Time-Series-ARIMA-XGBOOST-RNN

Time series forecasting for individual household power prediction: ARIMA, xgboost, RNN

Time-Series-Forecasting-

Consulting Project with Manifold.co: Modeling System Resource Usage for Predictive Scheduling

web-traffic-forecasting

Kaggle | Web Traffic Forecasting 📈