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.

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.

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.

A compact optical instrument with artificial neural network for pH determination.

The aim of this work was the determination of pH with a sensor array-based optical portable instrument. This sensor array consists of eleven membranes with selective colour changes at different pH intervals. The method for the pH calculation is based on the implementation of artificial neural networks that use the responses of the membranes to generate a final pH value.