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algoritmablog

Website for technical blog algoritma

awesome-r-dataviz

Curated resources about Data Visualization, Drawing & Publishing in R

business-management-using-r

Business Process Management, Six Sigma, etc

carbonr

Calculating carbon emissions in R

climate-data-visualization

Interactive CO2 emissions data visualization.

climate_emergency

Resources to make open data relating to climate change more discoverable for local councils

edx

Data Science courses in R from HarvardX

electricity-time-series-ml

A dataset containing time series data for electricity consumption for first 23 days of every month was given. We had to predict the electricity for the remaining 7 days of each month for each year.

energy-consumption-forecasting

energy-consumption-forecasting---multiple-seasonality

We seek to analyze the electricity consumption data and present the performance of basic forecasting models, STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods, ETS, ARIMA, TBATS and compare them.

energy-consumption-timeseries-forecasting

Comparing ARIMA, VAR, ANN and VAR-ANN hybrid models for forecasting energy consumption timeseries.

forecast_r-shiny

R-shiny application for forecasting

forecasting-energy-consumption-in-the-us

Forecasting Energy Consumption in the US

greeneconomy

machine-learning-using-r

Walk through, step by step examples of building different predictive models using R

modelling-electricity-demand-using-sarima-and-holt-winters

Electricity demand prediction in today's world plays a very crucial role as it helps plan load allocations and also helps in making decision beforehand regarding the upgradation of the infrastructure for the generation and transmission of electricity. Moreover, forecasting electricty’s demand greatly helps cut down costs as well, because it helps with better planning and decision making. Generally energy consumption time series predictions are difficult to model because of the presence of complex linear and non-linear patterns. In this project we will be using the Autoregressive Integrated Moving Average (ARIMA), seasonal ARIMA (SARIMA) and Holt-Winters method to forecast the future demand of electricity in Toronto.

nilmtk_readings

project_shinyapp

Data Visualization Project using R ShinyApp on The Global Carbon Footprint (1970 - 2015)

real-time-load-separation

Real-time appliance level load separation from total power usage.

reportnilmaccuracy

smartmeter-power-consumption-time-series-forecasting

Prediction of kW 48 hours ahead. Smart meter reading, real world untouched data of client.

smartmeterdata

Code for processing various smart meter data sets. No data here, just code

spring20-barandogru

spring20-barandogru created by GitHub Classroom

time-series-forecasting

Forecasting residential energy consumption using linear and Holt-Winters algorithms