Abstract
Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.
Highlights
In recent years, preserving the environment has become a great concern
In order to validate the innovative feature of the hybrid model, several artificial neural networks (ANNs), polynomial, and support vector regression (SVR) models have best experimented on using the whole dataset
The hybrid intelligent model described in this research predicts the output temperature of a solar panel, taking into account the input temperature, the flow through the panel, and the solar radiation
Summary
In recent years, preserving the environment has become a great concern. One of the reasons for this trend is environmental deterioration caused by human action. 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]. 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. The study focuses on the ability to accurately predict the amount of energy generated by a solar thermal installation This ability would make it possible to purchase only the amount of external energy required to meet the energy demand, thereby achieving optimal comfort, but spending only what is necessary. All the results are presented, and the conclusions are drawn and future lines of research are outlined
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