The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).

This paper aims to design and implement a decentralized multi-agent hierarchical system for energy management that can perform real-time monitoring and management of a real-world power grid with penetration of renewable energy. This approach integrates intelligent solutions based on intelligent agents to provide scalable and reliable management of smart power grids. The proposed decentralized multi-agent hierarchical system architecture allows for balancing multiple objectives, such as cost and environmental impact, in the design and operation of the energy system. The testing and tuning of this system are based on simulating real-time data flow and feedback between monitoring and control agents within a multi-agent environment modelling a smart grid. The added value of this study lies in its integrated approach to smart grid energy management, which combines real-time monitoring, decentralized control, hierarchical architecture, and consideration of both economic and environmental factors. Moreover, the use of multi-agent systems for simulation further enhances the adaptability and scalability of the system, and the focus on prosumers and the integration of renewable energy sources make it a relevant contribution to the field of sustainable energy management. While the results are promising, the current simulation framework is based on single-run experiments, limiting the statistical strength of outcome interpretations. Future research will address these aspects through expanded statistical validation, the inclusion of performance indicators, and deployment scenarios in more complex, real-world energy systems to enhance the robustness and applicability of the approach.

This paper proposes a Matlab object oriented application based on Kohonen Self- Organizing Maps (SOM) able to classify consumers’ daily load profile. Firstly, the characteristics of Kohonen self- organizing maps are briefly described in order to underline the advantages and disadvantages of these types of neural networks in classifications approaches. In the second part, data used for classification of load daily profiles is processed using statistical methods and Matlab. The result of these computations is a data base composed of daily load profiles used for SOM training. In the third part, the proposed software is tested on several scenarios in order to classify different consumers’ load profiles

The general context of this proposal is represented by the energy-efficient smart home that integrates renewable energy sources such as photovoltaic panels. The objective of this article is to minimize the amount of energy consumed from the national energy grid by producer-consumers of energy from renewable sources, in their own smart homes. In order to fulfill this goal, it was necessary to estimate the amount of renewable energy produced on the day-ahead horizon and to schedule the operation of controllable consumers in a smart home. To predict the amount of energy produced, two approaches were used: the first was based on data, and used techniques specific to artificial intelligence, more specifically, multilayer perceptron and radial-basis-function neural networks, and the second was based on models. The accuracy of the short-term prediction horizon of the techniques used was evaluated with quantitative performance indicators so that the most appropriate one in relation to the goal of the article could be selected to be used in the test scenarios. The scheduling of consumer functioning was based on their classification in relation to their ability to be controlled, and on the selection from the peer-reviewed literature of an optimization algorithm which, by load shifting from a smart home, ensured the optimal fulfillment of the objective function. The selected load-shifting algorithm was then integrated into and tested on a real database. The data used were monitored for two representative days, in terms of the amount of energy from renewable energy sources produced and consumed. The load-shifting algorithm proved its effectiveness through the results obtained and which are reported in the article.