## 7 Conclusions
### Future Work  
Future work on MIRD includes for the following research lines:  
1. NoSQL database implementation. The growing volume of the Parquet-based persistence layer of MIRD could eventually decrease the complete pipeline's performance. We must perform the required research to design a new persistence layer; proven strategies in this field point to replacing our Parquet-based layer with a faster and more efficient NoSQL database [30]. For this task, we must evaluate different NoSQL databases (column, key-value, document-based). 2. Parallel implementation. The data-preprocessing and the forecasting components of MIRD use SciKit Learn, designed to run on single-device hardware configurations. Given the massive amount of data managed by MIRD, it is advisable to distribute the SciKit Learn code or migrate those components to a computing framework that runs in distributed mode. Although many programming resources can distribute SciKit Learn jobs, Apache Spark, which already is the basis for the data-ingestion and the power-quality-evaluation components, is a convenient option upon which to base this upgrade. 3. Forecast-model enhancement. MIRD's forecasting models rely on MLP neural networks. Recently, we started experimenting with deep-learning models for MIRD based on long short-term memory (LSTM) networks, encoder-decoder networks, and transformers that have constantly reported better predictive performance than MLPs [31; 32; 33]. Our results suggest adding forecasting models based on such neural-network architectures to MIRD. However, having more powerful neural networks also means requiring more computing capacity. Therefore, this research line reinforces the need for achieving parallel implementation. 4. Migration to an ML-pipeline development framework. We started the MIRD project in mid-2017 with early and preliminary prototypes and forecasting models. At that time, the availability of a fully integrated, end-to-end, production-ready framework for ML-pipeline development was still an idea. Today, many widely tested, open-source frameworks for ML-pipeline authoring are available [34; 35; 36]. Given the solid technical support and implementation expertise associated with these frameworks, it is worth analyzing a migration of the complete functionality of MIRD to one of them. Author Contributions: The following are some of the contributions provided by the different authors. Conceptualization, J.J.F., J.L.G.-N. and A.M.; methodology, J.J.F. and F.C. ; software design, J.J.F. and J.L.G.-N.; forecasting models, J.J.F. and J.L.G.-N.; database design, J.R.C.G. and F.C. ; preprocessing, V.M.T. and J.L.G.-N.; state-of-the-art research, J.L.G.-N.; data curation, A.M., J.L.G.-N., J.R.C.G. and V.M.T. ; writing--original draft preparation, J.J.F. and J.L.G.-N.; writing--review and editing, J.J.F., J.L.G.-N., J.R.C.G. and V.M.T. ; project administration, J.J.F. and F.C. All authors have read and agreed to the published version of the manuscript. Funding: [PERSON]'s doctoral program has been funded by CONACYT National Scholarship under CVU No. 737505. [PERSON]'s doctoral program has been funded by CONACYT National Scholarship No. 516226/290379. [PERSON]'s doctoral program has been funded by CONACYT National Scholarship under CVU No. 816803. Conacyt (Consejo Nacional de Tecnologia, Av. Insurgentes Sur 1582, Col. Credito Constructor, Alcaldia Benito Juarez, C.P. 03940, Mexico City, Mexico) is the Mexican National Council for Science and Technology. Institutional Review Board Statement: Ethical review and approval were waived for this study, due to the fact that this article does not contain any studies with human participants or animals performed by any of the authors. Informed Consent Statement: Not applicable. Acknowledgments: The authors acknowledge all personnel of Comision Federal de Electricidad for their valuable engineering knowledge and the technical support we received during the development of the project MIRD. Conflicts of Interest: The authors declare no conflict of interest.