## 1 Introduction  
The electric energy needs are constantly growing. It is estimated that such demand will increment from 549 quadrillion British thermal unit (Btu), registered in 2012, to 629 quadrillion Btu in 2020. A further increment of 48% is estimated by 2040 [1]. The accurate estimation of the short-term electric energy demand provides several benefits. The economic benefits are evident because this would allow us to allocate only the right amount of resources that are needed in order to produce the amount of energy actually needed to face the actual demand [2,3]. There are also environmental aspects to consider, since, by producing only the right amount of energy required, the emission of \(CO_{2}\) would be reduced as well. In fact, energy efficiency is another relevant goal pursued with these kinds of approaches since the accurate forecasting of electricity demand in public buildings or in industrial plants usually leads to energy savings [4, 5, 6]. Such observations highlight the importance of being able to count on efficient electric energy management systems and prediction strategies and, consequently, different organizations around the world are taking actions in order to increase energy efficiency. Hence, the European Union (EU), under the current energy plan [7], established that EU countries will have to embrace various energy efficiency requirements with the objective of improving at least a 20% the energy efficiency. In addition to this, countries belonging to the EU closed an agreement to obtain an additional 27% increment of the efficiency by 2020, with the possibility of increasing the target to 30% by the year 2030. Forecasting algorithms could contribute to reaching such objectives [2; 3]. In this context, energy demand forecasting can be described as the problem of predicting the energy demand within a specified prediction horizon, using past data, or, in other words, a historical window. Depending on the time scale of the predictions, we can generally distinguish three classes of forecasting, i.e., short, medium and long-term forecasting. In short-term forecasting, the objective is to predict the energy demand using horizons going from one hour up to a week. If the prediction horizon is set between one week and one month, we talk about medium-term forecasting, while long-term forecasting involves longer horizons [8]. In this paper, we focus on the problem of short-term forecasting. This is an important problem, since with accurate predictions of short-term load it would be possible to make precisely plan the resources that need to be allocated in order to face the actual demand, which, as already stated, would have benefits from both the economical and environmental points of view. To this aim, we propose an extension of the work proposed in [9], where a deep feed-forward neural network was used to tackle the short-term load forecasting problem. In the original work, the tools provided by the H2O big data analysis framework were used along with the Apache Spark platform for distributed computing. Differently from [9], where a grid search strategy was used for setting the values of the deep neural network parameters, in this work, we propose to use a genetic algorithm (GA) in order to determine a sub-optimal set of hyper-parameters for building the deep neural network that will then be used for obtaining the predictions. Due to the large search space composed of all hyper-parameters of a deep learning network, and considering that the method should be scalable for big data environments, it has been decided to reduce the search range of the GA. For this reason, our proposal will not always be able to find the optimal set of hyper-parameters for the network, but ensures a competitive sub-optimal configuration. Our main motivation lies in the observation that the success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging problems, the architectures have become difficult to design by hand [10].