Browsing by Author "Zorita Lamadrid, Ángel Luis"

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A data-driven forecasting strategy to predict continuous hourly energy demand in smart buildings
(2021) Duque Perez, Oscar
Smart buildings seek to have a balance between energy consumption and occupant com-fort. To make this possible, smart buildings need to be able to foresee sudden changes in the build-ing’s energy consumption. With the help of forecasting models, building energy management sys-tems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland
Charge management of electric vehicles from undesired dynamics in solar photovoltaic generation
(2022) Zorita Lamadrid, Ángel Luis; Davila Sacoto, Miguel Alberto; Gonzalez Morales, Luis Gerardo; Aguirre Pardo, Ivania Carolina; Duque Pérez, Óscar; Hernández Callejo, Luis; Espinoza Abad, Juan Leonardo
Power generation from photovoltaic solar systems contributes to mitigate the problem of climate change. However, the intermittency of solar radiation affects power quality and causes instability in power grids connected to these systems. This paper evaluates the dynamic behavior of solar radiation in an Andean city, which presents rapid power variations that can reach an average of 7.20 kW/min and a variability coefficient of 32.09%. The study applies the ramp-rate control technique to reduce power fluctuations at the point of common coupling (PCC), with the incorporation of an energy storage system. Electric vehicle batteries were used as the storage system due to their high storage capacity and contribution to power system flexibility. The application of the control strategy shows that, with a minimum of five electric vehicle charging stations at the PCC, the rate of change of the photovoltaic can be reduced by 14%.
Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings
(2022) Hernández Deyslen, Mariano
Buildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely used to support demand management decisions, but these strategies require large data sets to achieve an accurate electric consumption forecast, so they are not commonly used for buildings with a short history of record keeping. Based on this, the objective of this study is to determine, through continuous hourly electricity consumption forecasting strategies, the amount of data needed to achieve an accurate forecast. The proposed forecasting strategies were evaluated with Random Forest, eXtreme Gradient Boost, Convolutional Neural Network, and Temporal Convolutional Network algorithms using 4 years of electricity consumption data from two buildings located on the campus of the University of Valladolid. For performance evaluation, two scenarios were proposed for each of the proposed forecasting strategies. The results showed that for forecasting horizons of 1 week, it was possible to obtain a mean absolute percentage error (MAPE) below 7% for Building 1 and a MAPE below 10% for Building 2 with 6 months of data, while for a forecast horizon of 1 month, it was possible to obtain a MAPE below 10% for Building 1 and below 11% for Building 2 with 10 months of data. However, if the distribution of the data captured in the buildings does not undergo sudden changes, the decision tree algorithms obtain better results. However, if there are sudden changes, deep learning algorithms are a better choice