Edgar Bahilo Rodríguez's repositories
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
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
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 📈