# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 4 Discussion  
In this work we present a methodology that is based on statistical analysis and involves the preprocessing of the time series data before addressing the problem of short term electric load forecasting. The methodology employs statistical learning and proper data transformation in an image-like format in order to benefit from the CNN-based models, which exploit stationarity and time locality. While the superiority and the efficiency of LSTM (based on the importance of the inherent memory in load time series) are widely accepted, we showed that CNN-based models present a credible alternative for short term forecasting problems when compared to LSTM algorithms (leveraging the temporal locality of load time series by analogy with image processing where space locality is exploited). The achieved accuracy, based on MAE and RMSE, indicates that the proposed CNN algorithm's performance was improved compared to the LSTM model. CNNs seem to achieve better accuracy when we lack of historical data, the scale of the energy entities is small and the forecast horizon is short (day -ahead forecast). Since the efficiency of the LSTM for long term dependencies is unquestionable, a hybrid LSTM-CNN model is worth examining in future research work, especially for multi-step method considerations. The presented methodology performs demand forecasting at low scale level and thus the forecasted time series can be used as input to various energy management algorithms at microgrid or house level, providing a useful tool for optimizing the operation of the energy entities at the grid edge. Even though LSTM is the most common model to tackle the load forecasting problem, we have proven here that CNN can outperform LSTM in cases where the number of the observations and the energy deployment are limited (loads in a household or in a small energy community) and the load patterns change dynamically. We conclude that if AMI and the relevant communications infrastructure exist, the CNN model is more suitable for forecasting loads of a house, office, building or even of an energy community.  
Figure 11: Actual and forecasted load (Trento). The actual load is depicted with red color and the forecasted time series with blue color.  
**Author Contributions:** This paper was a collaborative effort among the authors. [PERSON] and [PERSON], designed and performed experiments, analysed data, validated the results and co-wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] supervised the research and co-wrote the paper; [PERSON], [PERSON] and [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the EU H2020 research and innovation program under project CROSSBOW--CROSS BOrder management of variable renewable energies and storage units enabling a transnational Wholesale market (Grant No. 773430).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.