# MJZ_LatentBayesianMelding（镜像）

**Repository Path**: lewous/LatentBayesianMelding

## Basic Information

- **Project Name**: MJZ_LatentBayesianMelding（镜像）
- **Description**: Latent Bayesian melding for non-intrusive load monitoring (energy disaggregation)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-17
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


This repository provides an easy to use working implementation of Latent Bayesian Melding for NILM (energy disaggregation).
For more details refer to our NIPS 2015 paper: Latent Bayesian melding for integrating individual and population models (http://papers.nips.cc/paper/5756-latent-bayesian-melding-for-integrating-individual-and-population-models;
or http://arxiv.org/abs/1510.09130). 

##### Requirements
Mosek academic license (free)

I found that the following Mosek version works: mosek-7.1.15-py2.7. I have tried higher versions, but it did not work.

To install Mosek, fully follow the instruction from here: http://docs.mosek.com/7.1/toolsinstall/Linux_UNIX_installation_instructions.html.

You can find the Mosek package in this repository, but you need a license to use it.

##### How to implement the algorithm
Just run the script: demo_latentBayesianMelding.py

##### Contact

Contact: You may contact the first author at:
mingjun.zhong@gmail.com or mzhong@lincoln.ac.uk