# Application of Neuro-Fuzzy Techniques for Energy Scheduling in Smart Grids Integrating Photovoltaic Panels

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## Abstract

**:**

## 1. Introduction

## 2. Clustering and Classification Framework

#### 2.1. Clustering Concept

#### 2.2. Clustering Based on Self-Organizing Maps Neural Networks

#### 2.3. Clustering Based on Fuzzy Inference Systems

## 3. Energy from Renewable Sources Scheduling Approach

#### 3.1. Classification of Appliances Load Profiles

#### 3.2. Smart Home Energy Scheduling

_{i}, and finish no later than the due date d

_{i}. Every one of the appliances can process order i.

_{im}and P

_{im}. As a result, the goals may be formulated as follows: to reduce the processing costs of all of the orders, the important decisions in this scheduling problem are the assignment of orders to appliances, the order in which orders are placed on each appliance, and the start time for all orders.

_{TIME}time samples (e.g., the appliance begins), the energy scheduling problem [28] can be represented in this approach (see Equation (4)) as the minimization of a cost function:

## 4. Neuro-Fuzzy Knowledge-Based System

#### 4.1. Design of Neuro-Fuzzy Knowledge-Based System

#### 4.2. Simulation and Testing of Neuro-Fuzzy Knowledge-Based System

#### 4.3. Scheduling of Loads for Optimal Use of Energy

_{UL}) and that of the energy produced from RES (P

_{PV}), a gap could be observed between the moment when the energy was produced and the real initial behavior of the consumers, in the absence of scheduling. The difference (P

_{util}) was more evident in the case of the second scenario, when the amount of energy produced was smaller (Equation (10)).

_{util}= P

_{PV}− P

_{UL}

_{PV}(black), the consumption of uncontrollable loads (red), and the difference between power and consumption (blue), for both scenarios.

_{PV}; in red is the difference between the P

_{PV}, uncontrollable loads, and the washing machine consumption; and in blue is the amount of energy available after the dishwasher supply (Figure 12a).

## 5. Conclusions and Research in Progress

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 10.**The difference between P

_{PV}generated and the consumption of uncontrollable loads, for (

**a**) scenario 1; (

**b**) scenario 2.

Model of PV panel | PV, Luxor, 275 W, Polycrystalline |

Number of panels per module | 12 |

Nominal output power | P_{nom} = 275 W |

Number of cells | C = 60 |

Nominal current | I_{nom} = 8.77 A |

Nominal voltage | V_{nom} = 31.42 V |

Short circuit current | I_{sc} = 9.27 A |

Open circuit voltage | V_{oc} = 38.58 V |

Reference temperature | T_{ref} = 25 °C |

Number of solar panels connected in series | N_{S} = 12 |

Appliance | Type | Power (kW) | Operation Cycle Duration (h) |
---|---|---|---|

Dishwasher | Uninterruptible—independent | 1.4 | 1 |

Washing machine | Uninterruptible—dependent—consecutive | 1.5 | 2 |

Vacuum clener | Uninterruptible—independent | 1 | 1.5 |

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**MDPI and ACS Style**

Dragomir, O.E.; Dragomir, F.; Păun, M.; Duca, O.; Gurgu, I.V.; Drăgoi, I.-C. Application of Neuro-Fuzzy Techniques for Energy Scheduling in Smart Grids Integrating Photovoltaic Panels. *Processes* **2023**, *11*, 1021.
https://doi.org/10.3390/pr11041021

**AMA Style**

Dragomir OE, Dragomir F, Păun M, Duca O, Gurgu IV, Drăgoi I-C. Application of Neuro-Fuzzy Techniques for Energy Scheduling in Smart Grids Integrating Photovoltaic Panels. *Processes*. 2023; 11(4):1021.
https://doi.org/10.3390/pr11041021

**Chicago/Turabian Style**

Dragomir, Otilia Elena, Florin Dragomir, Marius Păun, Octavian Duca, Ion Valentin Gurgu, and Ioan-Cătălin Drăgoi. 2023. "Application of Neuro-Fuzzy Techniques for Energy Scheduling in Smart Grids Integrating Photovoltaic Panels" *Processes* 11, no. 4: 1021.
https://doi.org/10.3390/pr11041021