## 1 Introduction  
Lately, there has been a considerable increase in interest in developing automated power grid management and distribution systems. The transition toward decentralized renewable energy makes energy grid management rather complicated and exposed to uncertainty. The stochastic nature of renewable energy and high variability in the energy demand of small and medium-scale consumers makes it difficult to correlate the increasing energy demand and energy availability at the edge of the grid. Moreover, with the adoption of small-scale renewable energy, the consumers of energy are transformed into prosumers that can play an active role in the management of the smart grid [1]. To better plan the available energy sources, the energy grid management processes are relying on the short-term prediction of energy demand, production, or flexibility. Accurate energy prediction catalyzes the transformation of prosumers into active energy players into smart grids [2]. They can contribute to the stability and efficiency of the power grid by balancing the energy demand with the production in demand response programs, while incorporating, at a larger extent, renewable energy. There are many use cases related to energy trading, management, and optimization of microgrids or hybrid energy storage that require accurate predictions of energy or price with different time granularities and forecast horizons. Energy prediction is necessary to address problems related to optimal energy management, safety in distribution, and losses reduction. The energy consumption and production of a microgrid must be estimated in advance to better plan the available energy resources or storage usage. In the case of energy trading, the prediction of energy price helps in minimizing the energy costs or increasing the profit of energy prosumers [3; 4]. Energy forecasting engines integrated with grid controllers are studied for better energy management [5; 6; 7]. The prediction of price-based demand-response signal and energy consumption patterns are integrated with home energy management controllers to increase energy efficiency and reduce electricity bills. The optimal power flow strategies could be implemented by using model-based predictive control [8]. The prediction models can be extended to the integration of other flavors of energy, such as the thermal energy or water distribution [9; 10], and are useful in detecting abnormal behavior in energy consumption [11]. The significant increase in computing power and the evolution areas, such as the Internet of Things (IoT), Big Data, and Machine Learning, facilitate the development of intelligent management and control of energy systems [12; 13]. Smart energy meters or sensors can be easily installed in key points of the grid or at prosumers to monitor and collect relevant information for the management processes. They provide a constant flow of data to be used by intelligent systems in the prediction processes and to take decisions to enforce the security, safety, stability, and performance of the smart grid. The challenge is that the huge amount of energy data generated by all these measuring instruments must be collected and processed as soon as possible to extract estimates that will support the automatic decision process. There are two types of energy prediction models applicable for small and medium-scale prosumers white-box and black-box models [14]. The white-box prediction models use deterministic models [15] and feature a good generalization ability but do not feature large-scale applicability as they base on specific building data (e.g., construction details, operation schedules, HVAC design, etc.) [16]. The black-box models, on the other hand, use big-data-driven techniques. The models are trained using energy data collected by smart meters and are used to predict energy consumption. They have good applicability, as they do not depend on the characteristics of the prosumer but more on the energy data availability.