## 5 Conclusions and Research in Progress  
The objective of this research was to create a knowledge-based system, based on neuro-fuzzy and scheduling techniques, capable of ensuring the best use of en-ergy from RESs. The incorporation of tools based on neuro-fuzzy technology ensured the best possible distribution of green energy generated in a smart grid, which was represented by a household with a photovoltaic power system already installed. The context of smart grids integrating renewable energy sources, as well as the difficulties and prospects in the area, were described in the article's introduction. This article's second topic dealt with clustering and classification techniques. As a result, the neuro-fuzzy tools were chosen as the strategy suitable for estimating load consumption habits. In the third portion of the study, an algorithm for energy scheduling in a smart house was integrated into the spe-cific context of a smart home that had an installed power capacity of 3.3 kW. The hybrid system was introduced and tested in the fourth portion of the research. Its validation was completed, using an actual database. The data were observed between the 2 nd of February and the 5 January 2023, for two representative days in terms of the energy from RES produced and consumed. The find-ings acquired and provided in Section 4.3 served as evidence of the suggested algorithm's usefulness. The research reported here lacks a cost-based analysis of the sustainability of the smart grid system. This type of analysis should consider different sizes/technologies of PV panels integrated into smart grids, or different numbers of smart loads to shift. The cash inflow is directly impacted by the annual directly saved energy by self-consumption, the net metering contribution, and government contributions (eventual feed-in tariff tax deductions). Furthermore, the cash outflow depends on the investment costs of the smart grid and the annual maintenance costs. The maintenance costs for a residential plant are very low in comparison with the initial investment afforded, being equal to 0.5% of the initial investment cost. All of these financial aspects related to the difference between the discounted annual cash inflows and outflows were difficult to quantify for reporting. The limitation is due to a series of factors that we could not quantify: incomplete national legislation regarding prosumers, a dysfunctional liberalized green energy market, and a compensatory green certificate system that has not yet been implemented. All we can assume is that for the smart home's operational scheduling, the total daily net cost, i.e., costs for acquiring electricity from the grid minus revenues from selling renewable electricity to the grid, results in some revenues. However, the case study was conducted on a single-family smart home with an installed renewable energy capacity of 3.3 kW, and this framework placed a cap on the number of controlled loads that could be chosen. The National Energy Strategy, which provides a subsidy for this purpose and supports the installation of photovoltaic systems with an installed capacity of 3.3 kW in single-family homes, also established the framework. Finding energy management solutions for smart houses that guarantee the largest percentage of consumption from the homes' own production is, therefore, of greatest importance. The proposed smart grid that integrated PV panels can be upgraded with energy storage capacities so that the approach can involve multi-objective optimization. Furthermore, a future research approach that we want to address is the analysis of scheduling algorithm convergence times, and the carbon footprint that the implementation of the proposed neuro-fuzzy hybrid system generates. **Author Contributions:** [PERSON] and [PERSON] conceived the research idea and wrote the paper; [PERSON] and [PERSON] implemented the research; [PERSON] and [PERSON] performed the analysis. All authors contributed significantly to this research.