The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

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Micro-grid systems have been recently emerged for efficient integration and management of renewable energy sources, buildings’ equipment (e.g. ventilation; lighting; heating, ventilation, and air conditioning), and energy storage devices. The intermittent nature of the produced electricity from renewable energy sources constitutes, however, the main challenge for their seamless integration into buildings. Recent studies stated that storage devices, despite their actual high cost, could be used to tackle this issue by balancing between the variability of renewable energy sources production and the unpredictable building’s occupancy. However, managing the power flows in micro-grid systems is highly required in order to minimize the consumption from the electric grid while ensuring the quality and the reliability of electrical services. In this article, a micro-grid system platform is introduced for efficient integration and management of renewable energy sources and storage devices. The platform’s architecture is composed of three main components: (a) the power generation components that integrate the renewable energy sources together with the storage devices and the traditional electrical grid, (b) an advanced metering component for data gathering and analysis, and (c) control services for power flow management. The platform was developed and deployed in a real-setting scenario. Simulations and experimentations have been performed and results show the usefulness of the proposed platform for efficient management of the deployed micro-grid system.

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Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.

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