# Interlinking Heterogeneous Data for Smart Energy Systems

arXiv: https://arxiv.org/abs/1907.02790 · HTML: https://mlsanity.com/paper/1907.02790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [A predictive analytics approach for stroke prediction using machine learning and neural networks](https://mlsanity.com/paper/2203.00497/) (2022)
- [A semantic web approach to uplift decentralized household energy data](https://mlsanity.com/paper/2208.10265/) (2022)

## Abstract (arXiv)

Smart energy systems in general, and solar energy analysis in particular, have recently gained increasing interest. This is mainly due to stronger focus on smart energy saving solutions and recent developments in photovoltaic (PV) cells. Various data-driven and machine-learning frameworks are being proposed by the research community. However, these frameworks perform their analysis - and are designed on - specific, heterogeneous and isolated datasets, distributed across different sites and sources, making it hard to compare results and reproduce the analysis on similar data. We propose an approach based on Web (W3C) standards and Linked Data technologies for representing and converting PV and weather records into an Resource Description Framework (RDF) graph-based data format. This format, and the presented approach, is ideal in a data integration scenario where data needs to be converted into homogeneous form and different datasets could be interlinked for distributed analysis.