## 2 Related Work
### Self-Organizing Map (SOM)  
SOM was first proposed by [PERSON] in 1990 [27]. Since then, it has been applied to various fields including the load forecasting domain. Day-ahead hourly load forecasting was conducted using historical data corresponding to an area in central Spain from 1989 to 1999 [24]. The researchers first classified the historical data for each day according to its load profile by means of SOM. ANNs were then trained using each class. Finally, they performed the prediction by using previously trained recurrent neural networks. Their experimental results showed that the forecasting performance of their method was superior to statistical techniques in terms of accuracy and robustness.  
Other researchers carried out day-ahead hourly load forecasting using a two-stage hybrid network with SOM and a support vector machine (SVM) [11]. SOM was applied to cluster the input dataset into subsets and to separate the dataset into regular days and anomalous days. SVMs were then fitted using each cluster. They found this structure to be robust against different data types and the non-stationarity of load data. They used historical load data from a New York Independent System Operator as a case study, and compared the results of using a single SVM with their proposed method. Their method outperformed the single SVM model in terms of mean absolute error and mean absolute percentage error.  
[PERSON] et al. [28] presented MTLF to predict the daily peak load of the next month for which they proposed the SOM-SVR model. SOM was used to cluster the historical load data into two subsets in an unsupervised manner, and two epsilon SVRs corresponding to each subset were employed to fit the data and to make predictions. They used an electricity load dataset in the European Network on Intelligent Technologies competition, and benchmarked Malaysian and PJM electricity load datasets. Their practical application results demonstrated that their proposed method far outperformed previous methods in terms of accuracy.  
[PERSON] et al. [29] presented interesting work. They used SOM not only for clustering, for which it was used most commonly, but also as a valid forecasting engine. The experimental results showed that their model is competitive compared to results obtained with more commonly used techniques such as ANNs and SVR. They also proposed that this structure of the model can be considered as an initial approach to standardize the load forecasting process. An input selection method that was proven to significantly reduce MAPE was discussed.  
[PERSON] et al. [22] focused on STLF at the microgrid scale. Their proposed method was composed of three stages, of which the first was pattern recognition by SOM. After the first stage, input data are represented by their best matching units, and these units are clustered by means of a k-means clustering technique in the second stage. Finally, each cluster is fed into the corresponding ANNs. The case study was performed on data from the Spanish company Iberdola. The results produced lower errors compared to other simple models without clustering.  
[PERSON] et al. [23] carried out STLF in small-size loads (i.e., the buses of transmission and distribution systems). They conducted day-ahead and hour-ahead load prediction, and proposed models based on ANNs and SOM. Four models were proposed. Model A was a simple ANN model for day-ahead STLF and was constructed to perform benchmarking. Model B was a combination of Model A and SOM clustering. Model C was a simple ANN model for hour-ahead STLF for benchmarking. Model D added SOM clustering to Model C. The experimental results showed that the proposed models (B and D) produced superior forecasting accuracy.  
As can be seen in the above research, SOM has been successfully applied in the field of load forecasting. However, there has been no attempt to address the overfitting problem that may occur after clustering, especially in the context of having a small-size dataset and targeting small-scale systems. This motivated us to do this research.