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Forecast models play a fundamental role in anticipating the effects of the energy demand in buildings to addressing the energy crisis. A forecast model for anticipating from one to three days every 30 min of the building energy demand is presented. In this model, a feed-forward artificial neural network (ANN) is combined with bootstrap aggregation techniques, using a Box-Cox transformation, seasonal and trend decomposition using loess, and a moving block bootstrap technique. An analysis was conducted using the data provided by a building's energy demand; the data were collected during a period of four months, with readings every 10 s and averages of the values obtained every 30 min. The feed-forward neural-network method combined with bootstrap aggregation techniques consistently outperformed the forecasting accuracy of the original feedforward neural network through cross-validation in the root mean square error (RMSE) and the mean absolute percentage error. From cross-validation in-sample period, used for the initial Original Research Article

The events caused by the COVID-19 pandemic forced educators to transition to a fully online education system. Therefore, the implementation of project-based learning (PBL) in education is not an easy task. However, since PBL offers students many learning benefits, the aim of this paper is to conduct a bibliometric analysis to measure the performance of using PBL in engineering courses. Two computer tools have been used to carry out the analysis: a) Scopus as a performance analysis tool and b) SciMAT to carry out the bibliometric analysis of content from scientific maps. The results show that the topic with the highest performance for the period 2000-2015 corresponds to the topic of engineering educators, with a h-index of 19, 157 linked articles and, 4815 citations. However, the transition to digital teaching is shown in the period 2016-2021, where e-learning systems represent a cross-cutting theme in the field of research. Therefore, the close relationship of the PBL methodology with engineering educators and the transition to virtual education is shown. However, it requires the integration of e-learning platforms. This is closely related to the driving topics shown in the strategic map for the 2016-2021 period, such as: ensuring the quality of teamwork; properly monitor learning experiences; enhance the focus on engineering design; and work on planning the design of the curriculum, to involve all the activities and evaluations of the educational goals.