Data-driven approach is essential to the modeling of building thermal dynamics. It has been
widely applied in building operation optimization, energy management, system performance …

In response to the exponentially increasing need to analyze vast amounts of data, Neural
Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

In this paper a new approach for short-term load prediction in buildings is shown. The
method is based on a special kind of artificial neural network (ANN), which feeds back a part …

In this paper, next-day hourly forecasts are calculated for the energy price in the electricity
production market of Spain. The methodology used to achieve these forecasts is based on …

Precise Air temperature modeling is crucial for a sustainable environment. In this study, a
novel binary optimized machine learning model, the random vector functional link (RVFL) …

Reliable and timely fault detection and isolation are necessary tasks to guarantee
continuous performance in complex industrial systems, avoiding failure propagation in the …

As part of the OptiEnR research project, the present paper deals with outdoor temperature
and thermal power consumption forecasting. This project focuses on optimizing the …

A Smart Home is able to generate energy-related values such as electricity consumption,
temperature, or luminosity without higher infrastructure requirements. The main aim of this …

Artificial neural networks (ANN) are non-linear mapping structures analogous to the
functioning of the human brain. In this study, we take the ANN approach to model and …