Framework for collaborative intelligence in forecasting day-ahead electricity price

Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts

Optimal predictive control of water transport systems: Arrêt-Darré/Arros case study

This paper proposes the use of predictive optimal control as a suitable methodology to manage efficiently transport water networks. The predictive optimal controller is implemented using MPC control techniques. The Arrêt-Darré/Arros dam-river system located in the Southwest region of France is proposed as case study. A high-fidelity dynamic simulator based on the full Saint-Venant equations and able to reproduce this system is developed in MATLAB/SIMULINK to validate the performance of the developed predictive optimal control system. The control objective in the Arrêt-Darré/Arros dam-river system is to guarantee an ecological flow rate at a control point downstream of the Arrêt-Darré dam by controlling the outflow of this dam in spite of the unmeasured disturbances introduced by rainfalls incomings and farmer withdrawals

Ingeniería y aspectos técnicos de la digestión anaeróbica

El presente volumen es la continuación natural del libro de la misma colección dedicado a los aspectos bioquímicos y microbiológicos del proceso de digestión anaerobia. En él se abordan los aspectos tecnológicos de los reactores en los que la producción de biogás tiene lugar. Después de un capítulo inicial sobre la evolución de la implantación de la digestión anaerobia se dedican dos capítulos a conceptos básicos y aplicados de bioreactores. La aplicación de las bases teóricas se abordan con un enfoque diferenciado por tipología de materia prima a tratar: deyecciones ganaderas, fracción orgánica de residuos municipales, lodos residuales de plantas depuradoras, cultivos energéticos, mezclas de los substratos anteriores con residuos orgánicos industriales (codigestión) y aguas residuales, para las cuales la digestión anaerobia presenta ventajas ambientales, energéticas y económicas respecto a los sistemas aerobios convencionales para una gran variedad de efluentes industriales. Asimismo se abordan aspectos de automatización y control de las instalaciones, y de transformación de biogás para adecuarlo a su aprovechamiento energético (cogeneración, automoción o inyección a redes de gas natural), así como los sistemas usuales de pretratamiento de los substratos, para mejorar el perfil del proceso y aumentar la productividad energética. En algunos capítulos se aborda la evaluación económica de los proyectos o los sistemas de transformación de los efluentes (digestato) para mejorar su gestión y aprovechamiento. El libro presenta un enfoque técnico y didáctico y se considera que puede ser un complemento bibliográfico básico para estudiantes de cursos de ingeniería ambiental de carreras científicas y técnicas, aparte de un manual para ingenieros de proyectos y operadores de plantas de biogás.Postprint (published version

Framework for collaborative intelligence in forecasting day-ahead electricity price

Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts

The impact of the art-ICA control technology on the performance, energy consumption and greenhouse gas emissions of full-scale wastewater treatment plants

Advanced real time – Instrumentation, Control, and Automation (Art-ICA) controllers are an advanced control solution for biological nutrient removal wastewater treatment plants. Art-ICA has been previously shown to be capable of enhancing nutrient removal performance in BNR plants, at lower energy expenditures. However, the impact that this control solution has on the greenhouse gas emissions from full-scale wastewater treatment plants has not previously been addressed. This work addresses the effect of art-ICA on the performance, energy consumption and greenhouse gas emissions of two full-scale WWTPs, Chelas and Castelo Branco (Portugal). The raw wastewater, nitrous oxide emissions, energy consumption and water discharges were quantified in two independent trains operated under different operational modes, conventional operation and art-ICA control. The implementation of the art-ICA strategy improved the effluent quality and reduced the operational costs, resulting in a better performance of these WWTPs. The art-ICA controllers activation led to a reduction of 54% and 7–10% of the total nitrogen effluent and in the specific energy consumption, respectively. Moreover, process control with art-ICA did not have a negative impact on the NO emissions of the plants, and contributed to lower global warming potential by the facilities. The lower indirect carbon dioxide production due to lower energy consumption contributes to the observation that art-ICA control is environmentally preferable to conventional control