https://hdl.handle.net/20.500.12412/2538 Wed, 06 May 2026 00:16:51 GMT 2026-05-06T00:16:51Z https://hdl.handle.net/20.500.12412/7198 HIT Solar Cell Modeling Using Graphene as a Transparent Conductive Layer Considering the Atacama Desert Solar Spectrum Revollo, Henrry; Ferrada, Pablo; Martin, Pablo; Marzo, Aitor; Del Campo, Valeria The optical and geometrical properties of transparent conductive oxide (TCO) are crucial factors influencing the efficiency of a-Si:H/c-Si heterojunction (HIT) solar cells. Graphene is a potential candidate to be used as TCO due to its optical and electrical properties. Here, the effect of graphene as TCO is numerically analyzed by varying the number of graphene layers from one to ten. First, the optical properties are calculated based on the transmittance data, and then the HJT cell’s performance is simulated under the AM1.5 standard spectrum and the mean Atacama Desert solar spectral irradiance in Chile. In the modeling, the most relevant properties are calculated with the spectrum of the Atacama Desert. The most relevant values were obtained as follows: open circuit voltage Voc = 721.4 mV, short circuit current Jsc = 39.6 mA/cm2, fill factor FF = 76.5%, and energy conversion efficiency Ef f = 21.6%. The maximum power of solar panels irradiated with the Atacama Desert spectrum exceeds the results obtained with the AM1.5 standard spectrum by 10%. When graphene is the transparent conducting oxide, quantum efficiency has a higher value in the ultraviolet range, which shows that it may be convenient to use graphene-based solar cells in places where ultraviolet intensity is high. Thu, 17 Aug 2023 00:00:00 GMT https://hdl.handle.net/20.500.12412/7198 2023-08-17T00:00:00Z https://hdl.handle.net/20.500.12412/7185 Day-ahead scheduling in a local electricity market Sánchez de la Nieta, Agustín; Gibescu, Madeleine Local electricity markets offer new trading opportunities for existing and emerging actors in the energy sector. In this study, a mathematical model is created for the hourly scheduling of a day-ahead local electricity market. The local electricity market has local producers and consumers and a connection with a retailer. The local resources considered are solar PV production, and an energy storage system. Therefore, the local electricity market has local resources, the inflexible and flexible residential loads, and the connection to the distribution network. This connection is used for buying energy from the wholesale market through a retailer. For evaluating the feasibility of the scheduling in a local energy market, an optimization model is proposed in this study to maximize the operational profits from the local electricity market, which is managed by an energy service company (ESCO). There are revenues from selling the energy to all residential loads, while the costs come from buying the electricity and managing all the local resources. A case study illustrates the energy profiles of all the resources managed, which have an impact on revenues and costs of the local market. From the case study, we draw some practical conclusions about the impact of local resources on the local electricity market. Mon, 09 Sep 2019 00:00:00 GMT https://hdl.handle.net/20.500.12412/7185 2019-09-09T00:00:00Z https://hdl.handle.net/20.500.12412/7155 A self-calibration algorithm for soil moisture sensors using deep learning Aranda Brítez, Diego; Tapia Córdoba, Alejandro; Millán Gata, Pablo In the current era of smart agriculture, accurately measuring soil moisture has become crucial for optimising irrigation systems, significantly improving water use efficiency and crop yields. However, existing soil moisture sensor technologies often suffer from accuracy issues, leading to inefficient irrigation practices. The calibration of these sensors is limited by conventional methods that rely on extensive ground reference data, making the process both costly and impractical. This study introduces an innovative self-calibration method for soil moisture sensors using deep learning. The proposed method focuses on a novel strategy requiring only two characteristic points for calibration: saturation and field capacity. Deep learning algorithms enable effective and accurate in-situ self-calibration of sensors. This method was tested using a large dataset of simulated erroneous sensor readings generated with simulation software. The results demonstrate that the method significantly improves soil moisture measurement accuracy, with 84.83% of sensors showing improvement, offering a more agile and cost-effective implementation compared to traditional approaches. This advance represents a significant step towards more efficient and sustainable agriculture, offering farmers a valuable tool for optimal water and crop management, while highlighting the potential of deep learning in solving complex engineering challenges.; Se trata de la verión preprint del artículo. Se puede consultar la versión final en https://doi.org/10.1007/s10489-024-05921-0 Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12412/7155 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12412/7150 Self Calibration of Soil Moisture Sensors for Automatic Irrigation Systems Aranda Brítez, Diego Soil moisture monitoring is essential for efficient irrigation management in precision agriculture, yet sensor calibration remains a major bottleneck, often requiring labor-intensive manual adjustments. Inaccurate soil moisture readings due to sensor drift or manufacturing variability compromise irrigation efficiency and crop health. To address this, this thesis explores automated calibration strategies that combine machine learning and data assimilation techniques to enhance the reliability of soil moisture measurements. A first contribution is the development of a deep learning-based self-calibration algorithm that predicts the soil moisture sensor reading at field capacity using short sequences of uncalibrated sensor outputs. This model, built upon a hybrid CNN-DNN architecture, learns from synthetic data generated through HYDRUS-1D simulations across diverse soil textures and realistic hydrological conditions. By embedding soil-specific thresholds such as saturation and field capacity into the learning process, the approach eliminates the need for manual tuning and mitigates sensor drift, offering scalable calibration in real-world scenarios. In parallel, a data assimilation-based framework using the Iterative Ensemble Smoother and Particle Filter has been proposed. This method dynamically updates sensor parameters by integrating real-time field data with physically-based simulations. It enforces physical constraints on key variables, ensuring that updated parameters remain within soil-specific bounds. The approach demonstrates high adaptability to different environmental conditions and sensor error patterns, significantly reducing bias and improving irrigation performance. Both methods were experimentally validated under controlled and field conditions using an IoT-enabled infrastructure and high-precision reference instruments. The results confirmed the practical applicability, accuracy, and complementary strengths of the proposed approaches, establishing a solid foundation for scalable, autonomous soil moisture sensor calibration in smart irrigation systems. Sun, 01 Mar 2026 00:00:00 GMT https://hdl.handle.net/20.500.12412/7150 2026-03-01T00:00:00Z