**Abstract**

Fuzzy logic has been used in many fields, either to control a specific movement, improve the productivity of a machine, or monitor the work of an electrical or mechanical system or the like. In this chapter, we will discuss what are the basic factors that must be taken to use the fuzzy logic in the aforementioned matters in general, and then focus on its employment in the field of renewable energy. Three main axes for renewable energy are solar panels, a wind turbine and finally, solar collectors. The key to working and the basis of the static system is the mechanism for selecting the inputs that directly affect the output in addition to the methods and activation functions of the fuzzy logic.

**Keywords:** renewable, energy, solar panals, effeciency and optimization

#### **1. Introduction**

In this chapter a complete review of how the fuzzy dominant operates in renewable energy, analyzes, and control. Usually many of the words which are used arbitrarily in our lifestyle have a simple meening and big work. When representing or discus a system or any think, words were used such as big, small, long, short, cold, warm, hot, sunny, cloudy, fast, slow, etc., which are vague in nature. Humans are use unguaranti, catchy and muddy words when showing something or report a decisions to produce a certain actions. According the age, call were individual old, mid-age, young, old plus, and new young. Applying gas or stop pressure according to road situation, whether dry, slippery, sloping or flat. If the light level in the classroom is low, we increase the brightness with one touch, otherwise, we decrease it. These examples illustrate how our brain behaves and makes decisions during uncertain and ambiguous situations.

Studies of systems with unconfirmed and disinformation have reached the era of substitution with the submitting the article "Fuzzy Groups" by Lotfi Zadeh [1]. Although this text was first published in 1965, the use of Symbolic Logic (FL) increased after the latter half of the 1970s when Lotfi A. Zadeh two additional articles [2, 3], in which pure fuzzy mathematics was used for uncertain systems and decision making. FL apps have been gaining fast speed since the Japanese started using them in commercially available devices. Nowadays, it is possible to search for ambiguous applications in almost every region [4]. Depending The sustainable sources are containes more parameters and variables which difficult to control, but

using the artificial intellignt make the contol simple and easy to use. FL works in many areas of use for automatic control systems and monitoring issues [4–7]. It can be used in database control tool to manage the data flow and knowledge, huge data, innovative method, and smart work for the motors. Graphical works, signal prob., and body-motor simulation are also another applications where FL is function. Additionally, fuzzy is utilized as a mathmatics processor in some cases such as equation optimization, selecting, figures smoothing, etc. [4].

In this chapter, the mechanism of programming and designing mechanisms for improving the work of solar energy systems will be explained and how to make the most of their work. As for solar cells, as it is known, they have several uses, including: converting solar radiation into electrical energy, distillation, storage, heating water and so on from these applications. The initial design of these systems may be with a certain efficiency or at a specific degree of use with specific physical or material matters, here comes the role of methods for improvement, searching for weaknesses and addressing them in systems in general and solar systems in particular. Artificial intelligence, it has been used by many researchers in improving the work of solar systems in terms of increasing efficiency and choosing the values of the best variables at different times of the day in which the intensity of solar radiation, inclination angle, humidity, and the like changes.

The optimization process for the operation in general is based on the mathematical model reached in the initial design of the system. In an AI environment, the default system inputs are identified with the expected output of these inputs. When the ideal result for the system is available, the proposed controller, which is built on the basis of artificial intelligence, changes the internal factors of the system and adjusts the initial weights to make the general system work according to the expected standards and the productivity, as far as possible, is ideal.

#### **2. Fuzzy sets**

Fuzzy groups are the basic elements of FL. Fuzzy groups are characterized by organic functions. In fact, these organic functions are just kind of mysterious numbers. One must know the meaning of ambiguity in order to know the terms FL, membership function, and ambiguous number. For example, two specific colors are mixed in the color world and they appear in **Figure 1**. First, it's white, and then it's

**Figure 1.** *Blended colors in the universe of colors.*

*Functioning Fuzzy Logic in Optimizing the Solar Systems Work DOI: http://dx.doi.org/10.5772/intechopen.97107*

changed to black over a transition area so that it becomes light gray, gray, taupe, and black as we move from left to right. It is not a single color that is transparent in transition. Includes a black and white reminder and no one can distinguish one color from the other because the transitional part is blurred. Colors within the transition region are often highlighted as white, light gray, gray, dark gray, and black shown in **Figure 1**.

There are two color classes in **Figure 1**, black and white, called fuzzy subsets or fuzzy organic functions. The Fuzzy subset WHITE in **Figure 1** shows the tones that are white while the blur The BLACK subset represents the black hue. The prolonged changing zone, the tow different colors. The color is different from the two origin. The color range along the line is blurred. FL is used in the search for solutions in the common areas between ideal solutions and wrong solutions, and for this, the search process may produce unexpected and unexpected solutions. Most of the researchers used fuzzy logic to improve the productivity of a machine or improve the efficiency of a specific system or the like, due to the rule that this type of research is characterized by, in foggy areas, which allows the system to use solutions closer to reality than it was in the past. In mathematical equations, an example of the above, there is one or two roots in most possibilities. When using fuzzy logic, it will find a set of roots that give a much lower error rate which depends on the permittivity specified in the software.

#### **2.1 Fuzzy membership functions**

The functions of FMF can be seen as a tunnel between unconfirmed bits and a hazy outland. The fragile home of muddy information is subdivided and shown by unclear organic form, see **Figure 2**. Shades of gray become darker as we move from left to right or from white to black. Semi-color of gray were re-arranged into subsections as high, little high, low, very low and zero refered by the triple-kind mysterious organic method [1].

The organic function is the basis for the system modeling process in order to improve its operation or reduce the error rate. Where the number of functions must be determined with the quality of each function and according to the data that will be dealt with. There are linear and nonlinear models, each of them has a function quality that differs from the other, in order to reach the goal more quickly and accurately than is recognized. The organic functions that distinguish fuzzy groups and the groupings with which they are performed are the idea of fuzzy sets and systems of symbolic logic. Therefore, understanding ambiguous groups and their groupings is vital to understanding what is often done with fuzzy sets and symbolic logic. Therefore, this chapter is reserved for introducing ambiguous groups and

**Figure 2.** *Blended colors in the universe of gray colors.*

analyzing their properties from a control application point of view. Known membership functions will be reviewed to represent fuzzy groups one by one, and MATLAB functions for each will be written as a neighborhood to develop a symbolic, logical, user-defined toolbox.
