Associate Laboratory of Energy, Transports and Aeronautics

Funder

Organizational Unit

Publications

Study on thermal comfort by using an atmospheric pressure dependent predicted mean vote index

Publication . Ruivo, Celestino; Gameiro da Silva, Manuel; Broday, Evandro Eduardo

The thermal environment index Predicted Mean Vote is a descriptor largely applied to evaluate the comfort sensation of people in moderate environments. Software tools based on the Fanger's method have been created and used, which application is limited for the sea level atmospheric pressure. A procedure for estimating the index of thermal comfort of individuals in environments at air pressure different from barometric pressure 0 m of altitude was recently introduced. In present study, indoor air state associated with neutral thermal comfort conditions of individuals in seating activity are predicted for atmospheric pressure values in the range between 65.0 kPa and 101.3 kPa. It was observed that neutral temperature diminishes with the decrease of barometric pressure. Dependencies of index PMV on the activity level, the clothing insulation, relative, air velocity and on relative humidity for two atmospheric pressure values are investigated. It is concluded that the impact of pressure of the environment on the index of thermal comfort must be also considered when sizing an air-conditioning system.

2021Journal article

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The use of Monte Carlo method to assess the uncertainty of thermal comfort indices PMV and PPD: Benefits of using a measuring set with an operative temperature probe

Publication . Broday, Evandro Eduardo; Ruivo, Celestino; Silva, Manuel Gameiro da

The Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) are the most used indices for the assessment of thermal conditions in indoor environments. However, many times, the uncertainties associated with the calculation of both indices are not reported, may be because direct methods are not easily applicable to calculate it. The present study applies Monte Carlo method to assess the uncertainties on the calculation of PMV and PPD, as a function of values and the uncertainties of four environmental (air temperature, mean radiant temperature, air velocity, and partial vapour pressure) and two individual related input parameters (metabolic rate and clothing insulation), used in Fanger's model, to calculate it. The metrological quality of the measuring probes was assumed through the scenarios established by ISO 7726 (1998) (required and desirable conditions). The use of uncertainties values for metabolic rate, clothing insulation and operative temperature were also considered. The main findings of this research are: (i) condition defined as required is not suitable for implementation of the classification scheme of thermal environments proposed by ISO 7730 (2005); (ii) in desirable condition, it is unrealistic obtaining an uncertainty of 0.2 degrees C for mean radiant temperature, if a 0.2 degrees C uncertainty temperature probe is used; (iii) the use of an operative temperature probe is a good strategy to decrease the overall level of uncertainty in the indices.

2021-03Journal article

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A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zones

Publication . El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, Antonio; Rabehi, Abdelaziz

The increasing integration of solar sources into the energy mix presents significant challenges, particularly in short-term energy management. Accurate solar irradiance forecasts can greatly assist solar power plant operators and energy network managers in making informed decisions about energy production and consumption. This paper aims to develop a new accurate forecasting model for short-term global solar irradiance based on an innovative evolutionary forest approach. Our model, baptized EFITS, performs incremental tree selection through appropriate evolutionary operators maintaining a good tradeoff between accuracy and diversity, generating progressively near-optimal decision trees to construct the final evolutionary forest forecaster. This new evolution process also automatically selects near-optimal input parameters, enhancing the overall model accuracy and generalization ability. Six climatically diverse locations in Morocco and three types of inputs (endogenous, exogenous, and hybrid) are used to assess the performance of the proposed. The results demonstrate that our proposed model exhibits excellent performance across all studied sites and horizons. Among all input types, hybrid inputs delivered the best forecasting accuracy across all studied sites and horizons. Notably, the continental climate site (Bni Mellal) achieved the highest accuracy, with nRMSE ranging from 4.94% to 7.54% and nMBE from 0.71% to -0.46% for 1 to 6 h forecasts. Conversely, Ifrane city, characterized by a humid temperate climate, showed the lowest accuracy, with nRMSE ranging from 10.34% to 18.94% and nMBE from 1.21% to -1.54%. Finally, a detailed comparison with benchmarking models (random forest, bagging, gradient boosting, single decision tree, bidirectional long short-term memory network, and scaled persistence models), revealed that our model consistently outperforms them across all tested scenarios, locations, and forecasting horizons.

2024-06Journal article

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