# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 1 Introduction  
Smart meter data have already proved their importance for different players in the electricity sector. On the one hand, the transmission and distribution operators may have access to individual load profiles, allowing the estimation of future load demand for different areas. They provide the physical balance between supply and demand (at real time), and the opportunity to adjust the grid operation to minimize power losses, voltage drops (or other power quality events), thus increasing the grid resilience and reducing costs. For suppliers, it is also valuable to know in detail how the clients are effectively using this resource, estimating the expected accumulated electricity volume for their customer portfolio (at different hourly periods) when buying in the electricity market. Suppliers may also propose different commercial options (such as contracted power, the choice between flat/time of use/ or dynamic rates or even the counting cycles programmed in the meters) according to each consumer profile, launching fair rates, obtaining a distinct and valuable position as electricity retailers. Even end-users also take advantage of getting access to their own consumption, which should be viewed as a trigger to make demand more flexible, as this detailed information may undoubtedly increase awareness (when there are untypical consumptions, since alarmistic solutions are becoming popular). Furthermore, with the increasing and desired investment in renewable sources, traditional consumers are becoming prosumers and this phenomenon is expected to increase the interest of residential consumers (but not restricted to this sector) to rethink consumption behaviour, trying to match consumption with the available electricity from self-generation. The consequencesare less dependence on electricity provided by the grid, with the inherent savings in energy bills and the reduction in greenhouse gas (GHG) emissions. Through a comprehensive analysis of works developed in this domain, it becomes clear that several authors have made use of machine learning applications to estimate hourly electricity consumptions for the day-ahead, not only at a considerable aggregation level, but at a single household as well. Some references still use traditional statistical methods, such as Linear [1] or Polynomial Regression, Autoregressive Integrated Moving Average--ARIMA models [2; 3; 4] or some variants (such as incremental ARIMA or involving a prior stage of signal pre-processing) to enhance their performance. In [5], a model based on Conditional Kernel Density Estimation is compared with machine learning models. Despite the interest associated with these simple approaches, there are some drawbacks often referred to, such as the effect of multicollinearity among the input variables being used [6], and the models tend to become hostage and sensitive to the data quality, which is quite difficult to preserve in this kind of data series [7]. Machine learning algorithms tend to be transversal to a major part of the analysed references. In this domain, Artificial Neural Networks (ANN) are, by far, the most used algorithms [8]. This popular approach is a data-driven method [9] that does not need to be explicitly programmed [10]. ANN are often pointed out by their ability to learn and to identify hidden trends, thereby finding the intrinsic trends in time series [11]. Their ability to generalize even in the presence of incomplete and noisy data (common in residential smart meter data) [10] and their non-parametric distinction (they do not require prior assumptions about the data distribution) make them good approximators capable to model any continuous function at any desired accuracy. Some drawbacks associated with the use of ANNs in forecasting applications are the risk of getting underfitted (as it can get stuck on a local optimal solution) or overfitted models (if the training process is not interrupted through a proper cross-validation strategy) [12].