# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 1 Introduction
The proposed method outperformed ARIMA, SVR, and classical deep RNN on the Irish CER residential dataset. In [43] a spatio-temporal forecasting approach was proposed to leverage the sparsity which is a valuable element in small scale load forecasting. The proposed method combined ideas from compressive sensing and data decomposition to exploit the low-dimensional structures governing the interactions among the nearby houses. The dataset upon which the method was examined was the Pecan Street dataset. Additional categorization among the different load forecasting methods in the literature can be summarized as follows: (1) Physics principles-based models and (2) Statistical/Machine learning-based models. In [44] ANNs were used to perform load forecasting in buildings. Finally, in [45] a short-term residential load forecasting based on resident behavior learning was examined. RNNs, such as LSTM, seem as a reasonable selection for time series applications since they are developed explicitly to handle sequential data. In [46] a custom Deep Learning model that combines multiple layers of CNNs for feature extraction is presented, where both LSTM layers (prediction) and parallel dense layers (transforming exogenous variables) are proposed. Finally in [47] a novel method to forecast the electricity loads of single residential households is proposed based on CNNs (combined with a data-augmentation technique), which can artificially enlarge the training data in order to face the lack of sufficient data. This work focuses on a statistical learning-based approach by examining and leveraging the special statistical features of each given dataset [48] and thereafter transforming the dataset into a form according to the statistical analysis performed for that purpose. Our framework is based on the employment of a CNN architecture. CNNs are among the most popular techniques for deep learning, mainly for image processing tasks [49], where they leverage the spatial locality of pixels. There have been recently presented remarkable efforts of using CNN models for electrical load forecasting [50]. CNNs choice is based on the fact that in load time series, two neighboring points do not exhibit significant deviation. Therefore, load time series are characterized by temporal locality which can be exploited by CNNs. In this paper, we demonstrate that LSTMs for load forecasting under certain conditions can be outperformed; this finding is supported by experimental evidence. To prove our point, we furthermore compare our CNN model with three other machine learning techniques, namely LSTM, ANN and Multilayer Perceptron (MLP). The main contributions of the paper can be summarized in the following:  
1. We introduce a method for properly tuning the model's parameters by taking into account the time series statistical properties and especially its auto correlation. 2. We offer a valid alternative to the load forecasting problem in the case of low scale energy deployments where it is derived that our CNN based approach is more efficient than other methods. 3. We identify the conditions under which the CNN outperforms the other widely used forecasting solutions such as high temporal relationship between time series observations, lack of historical data and low scale load consumption. 4. We offer a solution to times series forecasting in cases where there is shortage of training data, since in our experiments the historical data available for training were limited. The rest of the paper is organized as follows: In Section 2 the proposed methodology is analyzed in detail, in Section 3 the results of our investigation is given and in Sectionwe present the discussion and the conclusions of our work and suggest possible future research extensions.