## 4 Conclusions  
Short-term electricity load forecasting has become one of the most popular research topic in recent years, and many inspiring types of research are seen in literature. Load forecasting has a leading role in the planning and management of power plants and production facilities. Research studies in the literature show that the load consumption pattern is highly related to air exogenous factors such as the weather condition, consumption time, day type (working, holiday etc.), seasonal effects, and economic or political changes. Therefore, several types of solution methods are required; here, these methods have also been improved in order to get better estimations.  
In the literature, there are many studies on load forecasting that use fuzzy logic and artificial intelligence optimization algorithms and methods. Here, these fuzzy models have been improved with expert insight and adaptations. Therefore, the ability and knowledge depth of the experts has directly affected the succession rate of the system. When the input variable and membership function sizes get larger, the knowledge base, rule base size, and number of rules also enlarge. So, the fuzzy model needs to be optimized, whether classical or artificial intelligence methods are used.  
In this paper, hybrid genetic-fuzzy and ant colony-fuzzy short-term load forecasting models were proposed. Artificial intelligence (AI)-based optimization methods were used to deal with the struggles in rule base definition that were mentioned above. The result show that intelligent optimization methods give promising results in load forecasting. The succession rate of the proposed system was measured by using MAPE, and the results showing that the proposed method has a 3.389% forecasting rate.  
As a result, we developed fuzzy logic-based load forecasting models for estimating hourly-based electricity consumption. The literature research and observations showed that the definition of the knowledge base is a major factor in having a successful forecasting rate. Moreover, when the size of input variables and/or membership functions size grows, the knowledge base becomes dramatically more complex. We developed GA-FL and AC-FL hybrid models using nature-inspired optimization methods to deal with knowledge complexity. The obtained results showed that the proposed models are more successful than the standard fuzzy logic approach that is typically seen in the literature, and proved that Nature-Inspired (NI) based methods help when working in a more flexible estimation environment.  
Figure 24: Comparative results for 29–30 January 2013.  
As a limitation of the proposed methods, in some cases, such as while working with a low-temperature period of time, some of the rules that are highly related to high-temperature periods may not be active in the optimization process. These inactive rules may cause an unnecessary increase in the optimization errors, and decrease optimization performance. In future studies, it is possible to develop new optimization techniques and algorithms for eliminating inactive rules.  
In future works, we will use other nature-inspired optimization algorithms for optimizing fuzzy forecasting models. We will develop new hybrid forecasting models using fuzzy type-2, and these models will be optimized with meta-heuristic optimization methods. Proposed new forecasting models will be trained and tested with large, scaled datasets.  
The manuscript was written by [PERSON] under the supervision of [PERSON] and [PERSON]. The modeling, simulation and analysis process was executed by [PERSON] and [PERSON]. Technical support was provided by [PERSON] and [PERSON], E.C. helped in the review.  
This research was supported by Scientific Research Projects Coordination Unit (B.A.P.K.B.) of Kirikkale University (Project No: 2016/127, 2017).  
The authors declare no conflict of interest.