nilm-eval-master
所属分类：其他
开发工具：matlab
文件大小：28007KB
下载次数：19
上传日期：2019-07-05 15:18:58
上 传 者：
Alex Ching Ho
说明： NILM-Eval是一个MATLAB框架，允许在不同场景中评估非侵入式负载监控算法，以全面了解其性能.NILM-Eval可以轻松评估多个数据集，住户，数据粒度，时间段和特定算法参数的算法。通过将这些参数封装在配置中。NILM评价仪进一步允许用户轻松地重复其他人执行的实验，评估新数据集上的算法，以及微调配置以提高新算法的性能设置
(Nilm-eval is a MATLAB framework that allows evaluation of non-invasive load monitoring algorithms in different scenarios to fully understand their performance. Nilm-eval can easily evaluate algorithms for multiple data sets, households, data granularity, time periods and specific algorithm parameters.By encapsulating these parameters in the configuration.NILM evaluator further allows users to easily repeat experiments performed by others, evaluate algorithms on new data sets, and fine-tune configurations to improve performance Settings for new algorithms)
文件列表:
Documentation (0, 2015-06-26)
Documentation\algorithm_configuration (0, 2015-06-26)
Documentation\algorithm_configuration\Baranski.txt (723, 2015-06-26)
Documentation\algorithm_configuration\Parson.txt (1031, 2015-06-26)
Documentation\algorithm_configuration\Weiss.txt (725, 2015-06-26)
Documentation\consumption_data.txt (3245, 2015-06-26)
HOWTO.txt (7413, 2015-06-26)
LICENSE (18025, 2015-06-26)
Matlab (0, 2015-06-26)
Matlab\algorithms (0, 2015-06-26)
Matlab\algorithms\baranski_alg (0, 2015-06-26)
Matlab\algorithms\baranski_alg\analyzeClusters.m (1574, 2015-06-26)
Matlab\algorithms\baranski_alg\baranski.m (6773, 2015-06-26)
Matlab\algorithms\baranski_alg\buildGraph.m (1878, 2015-06-26)
Matlab\algorithms\baranski_alg\buildSequences.m (1929, 2015-06-26)
Matlab\algorithms\baranski_alg\computeFitness.m (945, 2015-06-26)
Matlab\algorithms\baranski_alg\find_best_variation.m (2519, 2015-06-26)
Matlab\algorithms\baranski_alg\generateFSMs.m (832, 2015-06-26)
Matlab\algorithms\baranski_alg\getEvents.m (2605, 2015-06-26)
Matlab\algorithms\baranski_alg\getPlugEvents.m (968, 2015-06-26)
Matlab\algorithms\baranski_alg\my_fcm.m (4022, 2015-06-26)
Matlab\algorithms\baranski_alg\my_initfcm.m (807, 2015-06-26)
Matlab\algorithms\baranski_alg\solveConflicts.m (1179, 2015-06-26)
Matlab\algorithms\baranski_alg\writeParametersToTxt.m (1984, 2015-06-26)
Matlab\algorithms\fhmm_alg (0, 2015-06-26)
Matlab\algorithms\fhmm_alg\disag_fhmm.m (3212, 2015-06-26)
Matlab\algorithms\kolter_alg (0, 2015-06-26)
Matlab\algorithms\kolter_alg\generateHMMsFromSnippets.m (1241, 2015-06-26)
Matlab\algorithms\kolter_alg\generatePlevels.m (1014, 2015-06-26)
Matlab\algorithms\kolter_alg\generateSnippetsFromPlevels.m (1475, 2015-06-26)
Matlab\algorithms\kolter_alg\kolter.m (5105, 2015-06-26)
Matlab\algorithms\kolter_alg\myAfamap.m (3503, 2015-06-26)
Matlab\algorithms\kolter_alg\vec.m (384, 2015-06-26)
Matlab\algorithms\parson_alg (0, 2015-06-26)
Matlab\algorithms\parson_alg\find_training_ranges_generic.m (2795, 2015-06-26)
Matlab\algorithms\parson_alg\learn_params_generic.m (835, 2015-06-26)
Matlab\algorithms\parson_alg\make_dhmm.m (1568, 2015-06-26)
... ...
## NILM-Eval
NILM-Eval: An evaluation framework for non-intrusive load monitoring algorithms
## Project overview
NILM-Eval is a MATLAB framework that allows to evaluate non-intrusive load monitoring algorithms in different scenarios to gain a comprehensive view on their performance.
NILM-Eval makes it easy to evaluate algorithms on multiple data sets, households, data granularities, time periods, and specific algorithm parameters.
By encapsulating those parameters in configurations, NILM-Eval further allows the user with little effort to repeat experiments performed by others, to evaluate an algorithm on a new data set, and to fine-tune configurations to improve the performance of an algorithm in a new setting.
For more detailed information refer to the following sources:
* C. Beckel, W. Kleiminger, R. Cicchetti, T. Staake, S. Santini: [The ECO Data Set and the Performance of Non-Intrusive Load Monitoring Algorithms](http://www.vs.inf.ethz.ch/publ/papers/beckel-2014-nilm.pdf). Proceedings of the 1st ACM International Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys 2014). Memphis, TN, USA. ACM, November 2014.
* R. Cicchetti: [NILM-Eval: Disaggregation of real-world electricity consumption data](http://www.vs.inf.ethz.ch/res/project/eco-data-files/masters_thesis_chicchetti.pdf). Master's thesis, ETH Zurich, 2014.
* [The ECO dataset](http://www.vs.inf.ethz.ch/res/show.html?what=eco-data): Together with *Energie Thun*, a Swiss energy provider, we collected the ECO data set (Electricity Consumption and Occupancy). Using NILM-Eval, we evaluated the performance of four NILM algorithms on the ECO data set.
## Project team
NILM-Eval is a research activity of the [Distributed Systems Group](http://vs.inf.ethz.ch/) at ETH Zurich, Switzerland. It was initiated in the context of the project [Smart Meter Services](http://vs.inf.ethz.ch/res/show.html?what=smart-meter-services), in which we develop methods to analyze smart meter consumption data to offer novel services to households.
## Contact
If you have questions or ideas, contact [Christian Beckel](http://people.inf.ethz.ch/beckelc/).
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