- •Smart meter becomes the most researched area in the energy sector due to its benefits for consumers and utilities.
- •The need for powerful algorithms in smart meter data analytics increases as data grows.
- •In smart meter data analytics, performance of algorithms is improved by testing them with real-time data.
- •To test the effectiveness of developed algorithms, a large amount of real-time data is required.
- •Obtaining smart meter data sets is difficult due to privacy and security policies in many countries.
- •Synthetic data are widely utilized to mitigate the above problem without compromising privacy and security issues.
|Current code version||V1.1|
|Permanent link to code/repository used for this code version||https://github.com/SoftwareImpacts/SIMPAC-2022-132|
|Permanent link to reproducible capsule||https://codeocean.com/capsule/0796106/tree/v1|
|Legal code license||GNU General Public License (GPL)|
|Code versioning system used||git|
|Software code languages, tools and services used||Python|
|Compilation requirements, operating environments and dependencies||Python 3.9.12 |
|If available, link to developer documentation/manual|
|Support email for questions||mailto:[email protected]|
|Paper||Analytic framework used||Processing scheme||Seasonality and trend removal|
|Spark||AR||Centered moving average|
|Generative Adversarial Network (GAN)||ARIMA||–|
2. Software description
2.2.1 Pre-processing algorithm
2.2.2 Data generation
3. Impact overview
3.1 Real time applications
|Generating synthetic load patterns|
|Synthetic data generation in smart home|
|Consumer privacy mitigation|
|Non-intrusive load monitoring in households|
|Demand Data Generation for ML|
|Synthetic Electric Power Systems|
|Smart meter data analytics|
Declaration of Competing Interest
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