_Article_  
**Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization**  
**[PERSON] 1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,12,19,16,17,19,18,19,19,17,19,19,17,19,18,19,19,17,19,19,19,17,19  
became aware of the problem; many laws, directives, and treaties are made to try to lessen or even eliminate the problem. Changing the trend is a big challenge for various reasons, such as the countries' economic models, the current configuration of the cities, and people's habits. Many researchers have proposed solutions to the above-mentioned problems. An aspect that is becoming increasingly important is changing cities. In fact, some authors pointed to the importance of cities and their key role in fighting against climate change [1]. In this regard, within the smart city concept there are some key building-related terms, such as smart buildings. The definition of the term smart city is under constant review [2; 3]. Taking this fact into account, a smart city could be described as a city that uses the potential of technology and innovation, along with other resources that help make effective the use of this potential, promotes sustainable development, and ultimately, improves the quality of life of its citizens [4; 5; 6]. Smart cities are made up of a group of many elements, tangible or intangible [7]. One of those elements are smart buildings; they follow the same paradigm as smart cities, facilitating the achievement of their final objective [8]. Domotics or home automation, is an aspect of smart buildings. This term refers to the control of the different systems that a building includes: the air conditioning system, the lighting systems, the air purification system, the entertainment system, etc. [9]. In addition to home automation, smart buildings have the following smart city related features: sustainability, efficiency, security, etc. If possible, all these features should be part of a smart building from conception [10; 11; 12]. To accomplish all these objectives, it will be essential to employ multiple technologies [13; 14; 15; 16; 17]. To optimize aspects such as efficiency and sustainability, in addition to preventing unwanted loss, it is necessary to consume no more than the strictly required energy [16]. Given a house with the capacity to generate energy, it is necessary to know, beforehand, the amount of energy that will be generated and the amount of energy that will be consumed. This prediction would help ensure optimal comfort conditions. If the generation capacity is not able to meet the energy needs, the house's energy system must be supplemented by an external source [17]. For optimal energy generation, consumption, and purchase, it is essential to be able to predict all the variables satisfactorily [18]. In the state of the art, there are studies that address the topic of predicting the generation of solar energy [19; 20]. In [21], the authors propose a hybrid model that combines machine-learning methods with a theta statistical method for a more accurate prediction of future solar power generation from renewable energy plants. Reference [22] presented a solar power modeling method using artificial neural networks (ANNs), specifically, a general regression neural network (GRNN) and a feed forward back propagation (FFBP), using as inputs: maximum temperature, minimum temperature, mean temperature, and radiance. The main objective of [23] was to determine time horizon, having the highest representative for generated electricity prediction of small scale solar power system applications; finally it addressed solar power prediction in a 5 min time horizon. There are multiple prediction techniques which have been proven effective in prediction tasks. The different methods range from the simplest to the most complex ones, which frequently coincide with traditional and advanced methods, respectively.