Browsing by Author "Ferreira, Pedro M."
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- A comparison of energy consumption prediction models based on neural networks of a bioclimatic buildingPublication . Khosravani, Hamid Reza; Del Mar Castilla, Maria; Berenguel, Manuel; Ruano, Antonio; Ferreira, Pedro M.Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naive autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigacion en Energia SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.
- A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperaturePublication . Ferreira, Pedro M.; Gomes, João; Martins, Igor A. C.; Ruano, AntonioAccurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.
- An Intelligent Weather StationPublication . Mestre, Goncalo; Ruano, Antonio; Duarte, Helder; Silva, Sergio; Khosravani, Hamid Reza; Pesteh, Shabnam; Ferreira, Pedro M.; Horta, RicardoAccurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.
- Optimized design of neural networks for a river water level prediction systemPublication . Lineros, Miriam López; Luna, Antonio Madueño; Ferreira, Pedro M.; Ruano, AntonioIn this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrión river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 × 10−3 , which compares favorably with results obtained by alternative design.
- The IMBPC HVAC system: a complete MBPC solution for existing HVAC systemsPublication . Ruano, Antonio; Pesteh, Shabnam; Silva, Sergio; Duarte, Helder; Mestre, Gonçalo; Ferreira, Pedro M.; Khosravani, Hamid Reza; Horta, RicardoThis paper introduces the Intelligent MBPC (IMBPC) HVAC system, a complete solution to enable Model Based Predictive Control (MBPC) of existing HVAC installations in a building. The IMPBC HVAC minimizes the economic cost needed to maintain controlled rooms in thermal comfort during the periods of occupation. The hardware and software components of the IMBPC system are described, with a focus on the MBPC algorithm employed.The installation of IMBPC HVAC solution in a University building is described, and the results obtained in terms of economical savings and thermal comfort obtained are compared with standard, temperature regulated control. (C) 2016 Elsevier B.V. All rights reserved.
- Wireless sensors and IoT platform for intelligent HVAC controlPublication . Ruano, Antonio; Silva, Sergio; Duarte, Hélder; Ferreira, Pedro M.Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.