**6. Conclusions and Future Works**

This research study successfully developed a prototype SMH for monitoring electrical variables and uploading data to cloud using a wireless connection. This SMH was based on the Arduino open-source electronic platform and input data were collected with a set of sensors. The data are uploaded to the cloud each 0.5 s.

In addition, this study presents a web platform based on a cloud system that allows an innovative analysis of data captured in the Internet of Things data from smart homes, photovoltaics, and electrical vehicles in real time. Each user can download the data from their SM and view them in the developed app. The data are also available for query and download for the scientific community.

After the analysis of several solutions, we choose a cloud storage service for the subsequent and automatic processing of the data and the visualization, going from data to information. To validate the platform and present significant results, a case study was presented using the data acquired. The results of the experiments clearly show the benefit and feasibility of the proposed platform. We provided a detailed requirement analysis and illustration of the platform components.

The authors refined the platform component and it was tested with datasets from different electrical devices, such as household appliances. This approach is crucial to validate the platform's applicability and robustness in dealing with all types of IoT data measurements. This research uses datasets with extensive temporal data and with different types of households, allowing for comparisons of load profiles, electrical variables, etc.

Future lines of research would include adding datasets from different electrical devices, such as household appliances. In addition, future research would include developing a demand–response algorithm to be included in the SM to work in conjunction with the proposed methodology. Another possible line is aggregated information derived from measurements with application of artificial intelligence algorithms, e.g., automatic prediction systems and development of tools to perform a multidimensional analysis of the data.

**Author Contributions:** Conceptualization, M.A.G.-C.; methodology, A.C.-O. and F.S.-S.; software, M.A.G.-C.; formal analysis, A.C.-O. and F.S.-S.; investigation, A.C.-O. and F.S.-S.; resources, J.C.H.; writing—original draft preparation, A.C.-O. and F.S.-S.; writing—review and editing, J.C.H. and M.A.G.-C.; visualization, M.A.G.-C.; supervision, J.C.H. and M.A.G.-C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank the Department of Electrical Engineering of the University of Jaén for allowing the use of their laboratories and material in the development of this research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

