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

power-laws-forecasting

Winners of the Power Laws forecasting competition

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.

automl_service

Deploy AutoML as a service using Flask

Bokeh-Python-Visualization

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

cortana-intelligence-energy-demand-forecasting

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

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

economic_dispatch_sim

Lagrange multiplicators, linear regression for optimal energy dispatch

ElectricityDemandForecasting

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

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:

IndustrialScheduling

Optimal scheduling for industrial plants.

mlr

mlr: Machine Learning in R

MultiStepAheadForecasting

multi-step ahead forecasting of spatio-temporal data

Optimization-Pyomo

Linear and Nonlinear programing with Pyomo

power-laws-anomalies

price-forecast

PyomoGallery

A collection of Pyomo examples

PyomoGettingStarted

IPython notebooks that illustrate the Pyomo optimization modeling software

seasonalview

Graphical User Interface for Seasonal Adjustment

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

web-traffic-forecasting

Kaggle | Web Traffic Forecasting 📈