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Clean weather time series is the primary ingredient for the successful modeling of any process in the soil-plant-atmosphere continuum. However, measured meteorological data are often associated with gaps due to various reasons, such as eventual sensor malfunctioning, power outages, and data transmission errors. Thus, meteorological data needs to be...
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... stations are in center of Morocco, a country characterized by a Mediterranean semi-arid climate. A full description of the AWS is presented in Table 1. This issue is not an exception to our study area but is a widespread problem worldwide [14][15][16][17][18][19][20][21]. ...Context 2
... framework relies on machine learning models which are part of the DST library namely, Decision Tree [42] Evaluation of the filling method using correlation coefficients and RMSE values for í µí± í µí± , í µí± í µí± and í µí°» í µí± over different time intervals for the Graoua and Sidi Rahal AWS. Sidi Rahal AWS 1 day 1 week 1 month 1 day 1 week 1 month R RMSE R RMSE R RMSE R RMSE R RMSE R Where í µí¼ and í µí»½ are constants specific to the Magnus formula and equal to 17.625 and 243.04, respectively. To evaluate the gap-filling method, the Graoua AWS and Sidi Rahal AWS were used, the first one represents the most affected by data gaps (25.5%-25.96%) ...Citations
... The flowchart steps were implemented using Python language and DST [26] and ClimateFiller libraries [27]. ...
Accurately estimating latent heat flux (LE) is crucial for achieving efficiency in irrigation. It is a fundamental component in determining the actual evapotranspiration (ETa), which in turn, quantifies the amount of water lost that needs to be adequately compensated through irrigation. Empirical and physics-based models have extensive input data and site-specific limitations when estimating the LE. In contrast, the emergence of data-driven techniques combined with remote sensing has shown promising results for LE estimation with minimal and easy-to-obtain input data. This paper evaluates two machine learning-based approaches for estimating the LE. The first uses climate data, the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), while the second uses climate data combined with raw satellite bands. In-situ data were sourced from a flux station installed in our study area. The data include air temperatures (Ta), global solar radiation (Rg), and measured LE for the period 2015-2018. The study uses Landsat 8 as a remote sensing data source. At first, 12 raw available bands were downloaded. The LST is then derived from thermal bands using the Split Window algorithm (SW) and the NDVI from optical bands. During machine learning modeling, the CatBoost model is fed, trained, and evaluated using the two data combination approaches. Cross-validation of 3-folds gave an average RMSE of 27.54 W.nr² using the first approach and 27.05 W.nr² using the second approach. Results raise the question: Do we need additional computational layers when working with remote sensing products combined with machine learning? Future work is to generalize the approach and test it for other applications such as soil moisture retrieval, and yield prediction.
To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.
Numerical methods represent a powerful tool for weather forecasting. However, they still face various limitations related to energy consumption and the time it takes to run simulations. To overcome these weaknesses, various statistical and deep learning models were developed to combine precision, time, and energy efficiency criteria. In this paper, we evaluated models of both classes for the task of short-term weather forecasting (one day, three days, and one-week forecasts), namely, the autoregressive integrated moving average, the theta method, and fast Fourier transform as statistical models, versus a long short-term memory, neural basis expansion analysis for interpretable time series, and temporal convolutional neural network as deep learning architectures. The dataset used in this study is sourced from the automatic meteorological station installed in the Marrakech region (center of Morocco) covering the period from January 3, 2013, to December 31, 2020, on a half-hour scale. These include air temperature (Ta), air relative humidity (Hr), and global solar radiation (Rg). Before feeding the data to our models, we first used the ERA5-Land Reanalysis data to impute missing values found in our time series. Results show that the TCNN model outperforms the others in terms of the coefficient of determination (R2) and the root mean square error (RMSE).
