###### Abstract  
In recent years, most of the research in the field of smart grids integrating renewable energy sources assumed energy efficiency as a scheduling objective. However, the aspects of energy consumption or energy demand have not been described clearly, even though they have been proven to be an effective way of reducing energy consumption. In this context, this study aimed to cover a key research challenge in the field, such as the development of an intelligent strategy for solving energy consumption scheduling problems. The added value of our proposal consists of classifying individual consumption profiles assigned to each operation cycle phase, instead of considering an average of non-varying consumption of household appliances. Within this hybrid approach, the proposed explainable system, based on self-organizing maps of neural networks, fuzzy clustering algorithm, and scheduling technics, correlates the complex interrelation between power generated from renewable energy sources in a smart grid, prosumers' load behaviors, and the consumption profile of controllable or uncontrollable appliances. The tests were made using green energy consumption and production from real monitored data sets. The load-shifting algorithm that was used to reduce energy consumption from the national energy grid proved its effectiveness. In fact, consumers paid 25% less for the energy they used from the national energy grid during the times when the amount of electricity produced from renewable sources was reduced as a result of weather conditions.  
neuro-fuzzy; scheduling; renewable energy sources; power generation; loads 2023111021.11.11.11.1.11.11.1.11.1.11.1.  
The majority of RES are connected to the grid through some kind of power electronic interface [3], which allows the regulation of power delivered to the grid to achieve a balance between production and consumption.  
However, as the power generated by RES is highly variable, operations aiming to balance generation and consumption may result in inefficient use of RES and oversized electronic interfaces. The use of software interfaces is possible for certain loads, which allows for controlling the power demand, raising new possibilities in the operation of electrical grids [4].  
These new technologies bring new challenges and problems to deal with, such as ensuring proper integration of RES and energy storage into the electrical smart grids; achieving dynamic state estimation and observability of the novel smart power systems; taking advantage of existing and innovative technology and data efficiently and optimally; designing novel tools for stability and analysis development; and ensuring information exchange [5].  
In this context of smart grids, this study aimed to cover a key research challenge in the field, such as the development of a knowledge-based system based on neuro- fuzzy and scheduling technics, in order to ensure the optimal use of energy from RES. Within this hybrid approach, the proposed explainable system considered the complex interrelation between power generated from RES in a smart grid and load prosumers' (producers and consumers of energy from RES) behaviors. Precisely, the smart grid represented in this research is a household with an installed power system based on photovoltaic panels.  
This manuscript is organized as follows. Section 2 describes the clustering and classification framework, which supposes the identification of the proper machine learning techniques for the classification and clustering of load prosumers' behaviors. Section 3 introduces the mathematical formulation of the energy from the RES scheduling approach, correlated with the types of appliances used by prosumers. Section 4 describes the convergence of the proposed knowledge-based system with the main goal of the research. The efficiency of the proposed scheduling strategy relies on the numerical results computed using a real data benchmark, for two case studies. Finally, this study is concluded with Section 5.