## 1 Introduction
This prompted us to determine the factors that strongly influence the electricity consumption pattern of our target building and to decide which features to select. Previously, various data-driven techniques have been used to achieve higher performance by lowering the error rates in the load forecasting domain. They include autoregressive models such as ARMA [13] or ARIMA [14], which analyze historical data points in times series that are highly correlated with the future value, and statistical regression models such as multiple linear regression (MLR) [15; 16], which estimates the relationship between the input (i.e., features) and output variables. Machine learning models such as artificial neural networks (ANNs) [17], which emulate the structure of biological neural networks and can approximate complex and nonlinear functions, and support vector regression (SVR) [18], which finds the flattest function with the largest epsilon-deviation with regard to an entire training dataset and maps the input space to higher dimension using a kernel function to process nonlinear problems, are also included. Attempts to combine different techniques for improved performance were also reported. An enhanced version of empirical mode decomposition was employed to improve the Elman neural network on small-scale building load forecast. ARIMA was used to initially forecast the linear basic part of the load, after which SVR was used to correct the deviation of former forecasting [19]. Extensive and dedicated reviews are available and well described [3; 7; 20; 21]. The clustering technique, self-organizing map (SOM), has also been successfully employed prior to the forecasting task to improve the performance [11; 22; 23; 24]. In the first stage, SOM is used to cluster the training set into separate subsets in an unsupervised scheme, such that the subsets have similar patterns and characteristics. Subsequently, the same number of forecasting models is trained by each subset to take advantage of all past information and similar dynamic properties [11]. However, in the case of buildings for which the accumulated load data are insufficient because equipment such as AMI was only installed recently, clustering techniques have limited use. Once clustering is complete, the number of samples in each cluster is apparently smaller than the total number of samples in the entire dataset. For instance, if a dataset that has 1000 data points were clustered into four clusters, each cluster could contain 300, 200, 100, and 400 data points, which is smaller than their original size. The reduced number of samples would negatively affect forecasting models in the second stage of the forecasting procedure. Clusters with a small size could result in the \"overfitting\" problem, which refers to the situation in which a model fits the training set too closely or even contains noise such that it fails to predict unseen data (i.e., the testing set) and loses its generalization ability. This problem is exacerbated as the number of dimensions of a feature or the complexity of the model increases. Moreover, when dealing with small-scale systems such as buildings that have more noise signals and fluctuations, the chances that overfitting occurs increases because of intrinsic randomness (a larger-scale system aggregates a number of small-scale systems and offsets their randomness). To overcome this obstacle, we propose applying an ensemble technique to each cluster rather than employing a single model. Ensemble techniques, which use multiple forecasting models to improve the forecasting performance beyond that possible for any single model, have been successfully used in the time series forecasting and load forecasting domains [21]. They are well known to have excellent generalization ability, which means they are able to reduce overfitting [25]. Especially, among the ensemble techniques, we used the stacking ensemble method [26].