# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 1 Introduction
The use of ensemble methods is being substantially considered in the bibliography. Ref. [20] propose the combination of ANN with RF models, ref. [21] combines RF with Linear Regression and ref. [18] propose a hybrid method combining Random Forest and Multilayer Perceptron. An ensemble method combining several single models including Auto-regressive, Multilayer Perceptron, Extreme Learning Machine, Radial Basis Function and Echo-State Network is proposed by [7]. Refs. [14; 15] combine different approaches involving deep learning. Despite the generic trend to improve forecasting accuracy, hybrid methods are considerably more time-consuming (when scalability has a key role) and lacks for interpretability and explainability. Regarding error metrics, it is quite controversial to compare different works, as the datasets are obtained in different parts of the world (as access to smart meter data is becoming a reality in several developed countries) and the consumption patterns and volumes can be quite distinct. Ref. [11] describes Mean Squared Errors from 0.1 to 0.13 kWh2 for different hours being predicted and an accuracy from 52% to 70% (assuming a tolerance error within 10%). Ref. [21] reveal a Root Mean Squared Error of about 0.704 kW when predicting active power for the day-ahead with 15 min resolution. Ref. [14] shows Mean Absolute Percentage errors varying from 55.8% to 36.75% with the proposed hybrid model. Ref. [7] noted a reduction from 15.7% to 13.54% in the Mean Absolute Percentage Error metric when applying their proposed ensemble method. Ref. [22] proposes a federated learning algorithm with recurrent neural networks conducted with residential consumers, leading to a MAPE of about 17%. Different research has been applied in this domain, highlighting the importance of finding suitable forecasting models to be applied at household consumption level. Due to the inherent randomness and noise associated with residential consumption profiles, this task is often considered more challenging than forecasting at a level with more consumption aggregation (such as that of a public substation or even a national transmission grid). The main contributions of this study can be summarized as follows:  
* A comparison among different forecasting approaches: different alternatives were chosen to allow accurate predictions, also being interpretable models, providing feasible training times and easy to replicate. * A comparison between different alternatives not applied to a single consumer with specific characteristics, but for a larger number of consumers to allow a fair and extended comparison of created models: load patterns available in the used dataset are diverse; thus, scalability and flexibility are providential tools for the proposed forecasting methods. A detailed analysis of the created forecasting models, allowing the interpretation of the different feature contributions and a comparison of the training times. The article is organized as follows: Section 2 describes the used dataset for this study and presents some background related to the used methods and the strategies adopted when applying them in this study, and the list of features considered is also introduced. Section 3 is initiated with the description of the adopted error metrics, followed by the main results presentation, providing a detailed comparison among the proposed methods and allowing a comprehensive analysis of the main influential features in each case. Finally, in Section 4, the main conclusions are drawn, revealing the potential amongst the forecasting models tested and highlighting a comparative analysis of these selected models. In this section, some topics are proposed for further research.