Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies.

Time series forecasting still awaits a transformative breakthrough like that happened in computer vision and natural language processing. The absence of extensive, domain-independent benchmark datasets and standardized performance measurement units poses a significant challenge for it, especially for photovoltaic forecasting applications. Additionally, since it is often time domain-driven, a plethora of highly unique and domain-specific datasets were produced. The lack of uniformity among published models, developed under diverse settings for varying forecasting horizons, and assessed using non-standardized metrics, remains a significant obstacle to the progress of the field as a whole. To address these issues, a systematic review of the state-of-the-art literature on prediction tasks is presented, collected from the Web of Science and Scopus databases, published in 2022 and 2023, and filtered using keywords such as "photovoltaic," "deep learning," "forecasting," and "time series." Finally, 36 case studies were selected. Before comparing, a state-of-the-art demonstration of key elements in the topic was presented, such as model type, hyperparameters, and evaluation metrics. Then, the 36 articles were compared in terms of statistical analysis, including top publishing countries, data sources, variables, input, and output horizon, followed by an overall model comparison demonstrating every proposed model categorized into model type (artificial neural network units, recurrent units, convolutional units, and transformer units). Due to the mostly utilization of specific private datasets measured at the targeted location, having universal error metrics is crucial for clear global benchmarking. Root Mean Squared Error and Mean Absolute Error were the most utilized metrics, although they specifically demonstrate the accuracy relative to their respective sites. However, 33% utilized universal metrics, such as Mean Absolute Percentage Error, Normalized Root Mean Squared Error, and the Coefficient of Determination. Finally, trends, challenges, and future research were highlighted for the relevant topic to spotlight and bypass the current challenges.

A parsimonious methodological framework for short-term forecasting of groundwater levels.

Groundwater plays a significant role as a strategic resource in reducing the impact of droughts. In spite of its importance, there are still many groundwater bodies in which there is not enough monitoring data to define classic distributed mathematical models to forecast future potential levels. The main aim of this study is to propose and evaluate a novel parsimonious integrated method for the short-term forecasting of groundwater levels. It has low requirements in term of data, and it is operational and relatively easy to apply. It uses geostatistics, optimal meteorological exogenous variables and artificial neural networks. We have illustrated our method in the aquifer "Campo de Montiel" (Spain). The analysis of optimal exogenous variables revealed that, in general, the wells with stronger correlations with precipitation are located closer to the central part of the aquifer. NAR, which does not consider secondary information, is the best approach for 25.5 % of the cases and is associated with well locations with lower R2 between groundwater levels and precipitation. Amongst the approaches with exogenous variables, the ones that use effective precipitation have been selected more times as the best experiments. NARX and Elman using effective precipitation had the best approaches with 21.6 % and 29.4 % of the cases respectively. For the selected approaches, we obtained a mean RMSE of 1.14 m in the test and 0.76, 0.92, 0.92, 0.87, 0.90, and 1.05 m for the forecasting test for months 1 to 6 respectively for the 51 wells, but the accuracy of the results can vary depending on the well. The interquartile range of the RMSE is around 2 m for the test and forecasting test. The uncertainty of the forecasting is also considered by generating multiple groundwater level series.

Printed disposable colorimetric array for metal ion discrimination.

One of the main limiting factors in optical sensing arrays is the reproducibility in the preparation, typically by spin coating and drop casting techniques, which produce membranes that are not fully homogeneous. In this paper, we increase the discriminatory power of colorimetric arrays by increasing the reproducibility in the preparation by inkjet printing and measuring the color from the image of the array acquired by a digital camera, using the H coordinate of the HSV color space as the analytical parameter, which produces robust and precise measurements. A disposable 31 mm × 19 mm nylon membrane with 35 sensing areas with 7 commercial chromogenic reagents makes it possible to identify 13 metal ions and to determine mixtures with up to 5 ions using a two-stage neural network approach with higher accuracy than with previous approaches.

Feasibility of the use of disposable optical tongue based on neural networks for heavy metal identification and determination.

This study presents the development and characterization of a disposable optical tongue for the simultaneous identification and determination of the heavy metals Zn(II), Cu(II) and Ni(II). The immobilization of two chromogenic reagents, 1-(2-pyridylazo)-2-naphthol and Zincon, and their arrangement forms an array of membranes that work by complexation through a co-extraction equilibrium, producing distinct changes in color in the presence of heavy metals. The color is measured from the image of the tongue acquired by a scanner working in transmission mode using the H parameter (hue) of the HSV color space, which affords robust and precise measurements. The use of artificial neural networks (ANNs) in a two-stage approach based on color parameters, the H feature of the array, makes it possible to identify and determine the analytes. In the first stage, the metals present above a threshold of 10(-7) M are identified with 96% success, regardless of the number of metals present, using the H feature of the two membranes. The second stage reuses the H features in combination with the results of the classification procedure to estimate the concentration of each analyte in the solution with acceptable error. Statistical tests were applied to validate the model over real data, showing a high correlation between the reference and predicted heavy metal ion concentration.

A surface fit approach with a disposable optical tongue for alkaline ion analysis.

A disposable optical tongue for the alkaline ions Na(I) and K(I) is described. The two-sensor layout prepared on a transparent support consists of non-specific polymeric membranes working by ionophore-chromoionophore chemistry. The non-specific behavior of the membranes was controlled by means of the crown ether-type ionophore present. The imaging of the tongue, after reaction for 3 min with the unknown solution, by means of a conventional flatbed scanner working by transmission mode, makes it possible to calculate the H (hue) value of the hue, saturation, value (HSV) color space used as a robust and precise analytical parameter. The modelling of the response of the two-sensor tongue as a sigmoidal surface is used to characterize the behavior of the tongue and as a basis to infer the concentration values. To compute the concentration of two analytes from the two hue values obtained using the optical tongue, a surface fit approach was used. The tongue works over a wide dynamic range (1.0×10(-4)-0.1 M both in Na(I) and K(I)). The sensing membranes show good intramembrane (1.4% RSD) and intermembrane precision (0.71% RSD) and lifetime (around 45 days in darkness). The procedure was used to analyze Na(I) and K(I) in different types of natural waters (tap and mineral), validating the results against a reference procedure.

Full-range optical pH sensor based on imaging techniques.

A new colour-based disposable sensor array for a full pH range (0-14) is described. The pH sensing elements are a set of different pH indicators immobilized in plasticized polymeric membranes working by ion-exchange or co-extraction. The colour changes of the 11 elements of the optical array are obtained from a commercial scanner using the hue or H component of the hue, saturation, value (HSV) colour space, which provides a robust and precise parameter, as the analytical parameter. Three different approaches for pH prediction from the hue H of the array of sensing elements previously equilibrated with an unknown solution were studied: Linear model, Sigmoid competition model and Sigmoid surface model providing mean square errors (MSE) of 0.1115, 0.0751 and 0.2663, respectively, in the full-range studied (0-14). The performance of the optical disposable sensor was tested for pH measurement, validating the results against a potentiometric reference procedure. The proposed method is quick, inexpensive, selective and sensitive and produces results similar to other more complex optical approaches for broad pH sensing.

A real-coded genetic algorithm for training recurrent neural networks.

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.