Welcome to enlopy’s documentation!

Version:0.1.dev9
Date:Mar 30, 2019

Contents

enlopy is an open source python library with methods to generate, process, analyze, and plot energy related timeseries.

While it can be used for any kind of data it has a strong focus on those that are related with energy i.e. electricity/heat demand or generation, prices etc. The methods included here are carefully selected to fit in that context and they had been, gathered, generalized and encapsulated during the last years while working on different research studies.

The aim is to provide a higher level API than the one that is already available in commonly used scientific packages (pandas, numpy, scipy). This facilitates the analysis and processing of energy load timeseries that can be used for modelling and statistical analysis. In some cases it is just a convenience wrapper of common packages just as pandas and in other cases it implements methods or statistical models found in literature.

It consists of four modules that include among others the following:

  • Analysis: Overview of descriptive statistics, reshape, load duration curve, extract daily archetypes (clustering)
  • Plot: 2d heatmap, 3d plot, boxplot, rugplot
  • Generate: generate from daily and monthly profiles, generate from sinusoidal function, sample from given load duration curve, or from given PSD, add noise gaussian and autoregressive noise, generate correlated load profiles, fit to analytical load duration curve
  • Statistics: Feature extraction from timeseries for a quick overview of the characteristics of any load curve. Useful when coupled with machine learning packages.

This library is not focusing on regression and prediction (e.g. ARIMA, state-space etc.), since there are numerous relevant libraries around.

The documentation is under development. Please check the source code, the Reference/API or the example jupyter notebook in the github repository for feature details.

Indices and tables