## 7 Conclusions
### Discussion and General Conclusions  
PQ indices forecasting has gained attention due to increasing PQ deterioration factors in electric power systems, such as distributed, renewable-energy-based generation units or non-linear electronic devices. Most of the recent research on PQ forecasting uses relatively small datasets. The data may come from domestic or semi-industrial, off-grid electric-power configurations or from gathering information from only a few PQM in power distribution grids; some of them even come from simulation data. The state-of-the-art disregards the big-data nature of PQ forecasting for city- or regional-size grids, focusing mainly on harmonic distortion (voltage total harmonic distortion--THDv) at a fine-grained level of detail.  
This paper presents MIRD, a large-scale ML system for short-term PQ indices forecasting implemented at a Mexican, region-sized power distribution grid. MIRD is an early-warning system that produces one-week-ahead predictions at hourly resolution for the three-phase current unbalance and the power factor of nearly 700 distribution circuits. It also forecasts the operation voltage of nearly 150 quality-control nodes. This problem accounts for more than 4000 uni-variate forecast models executed weekly over massive amounts of data.  
MIRD operates on massive amounts of real data (sensed by IoT devices in the network), produces a large number of trained ANN-based forecasting models, and monitors the distribution network autonomously. Such a system has no precedent in the Latin-American electrical industry.  
In developing the system we present in this article, we tested several forecasting models and selected the type of model that best served the forecasting goals and could be implemented on premises with the available resources. Given those conditions, we could not afford more sophisticated models such as LSTM ANN, which take much longer to train and do not improve accuracy by much. Those facts lead to the decision to use feed forward  
Figure 22: Variables distribution for SMAPE \(\geq\) 20%.  
multi-layer perceptron networks as a general model with acceptable forecasting accuracy and which are not as computationally expensive as other models.  
Experimental results show that, although non-linearity in the underlying system that produces the data makes predicting reactive power (kVAR) a difficult task, MIRD's overall predictive performance is encouraging. A total of 63% of the forecasting models produce SMAPE values under 8.33%, while 82% of the forecasting models produce SMAPE values under 16.66%. Given the uniqueness of the system we present here, we cannot compare its results against other systems; the authors did not find any such systems reported in the literature.  
Most governments have issued strict PQ standards that power distribution grids must follow. PQ forecasting becomes crucial to ubiquitous early-warning PQ deterioration systems. However, to properly approach this task, it is essential to bear in mind the large-scale nature of the data when it comes to power grids that serve millions of customers. The practical value of MIRD lies in that, by reporting the likely behavior of each circuit and node one week in advance, MIRD allows the distribution engineers to produce corrective measures before delivering low-quality energy to the users. The delivery of high-quality energy by CFE allows the company to reach world-level standards. In addition, by maintaining the energy quality within the required intervals, CFE avoids expensive fines by the Mexican regulatory organization (CRE).