Wind speed and global radiation forecasting based on differential, deep and stochastic machine learning of patterns in 2-level historical meteo-quantity sets

Accurate forecasting of wind speed and solar radiation can help operators of wind farms and Photo-Voltaic (PV) plants prepare efficient and practicable production plans to balance the supply with demand in the generation and consumption of Renewable Energy (RE). Reliable Artificial Intelligence (AI) forecast models can minimize the effect of wind and solar power fluctuations, eliminating their intermittent character in system dispatch planning and utilization. Intelligent wind and solar energy management is essential in load scheduling and decision-making processes to meet user requirements. The proposed 24-h prediction schemes involve the beginning detection and secondary similarity re-evaluation of optimal day-data sequences, which is a notable incremental improvement against state-of-the-art in the consequent application of statistical AI learning. 2-level altitude measurements allow the identification of data relationships between two surface layers (hill and lowland) and adequate interpretation of various meteorological situations, whose differentiate information is used by AI models to recognize upcoming changes in the mid-term day horizon. Observations at two professional meteorological stations comprise specific quantities, of which the most valuable are automatically selected as input for the day model. Differential learning is a novel designed unconventional neurocomputing approach that combines derivative components produced in selected network nodes in the weighted modular output. The complexity of the node-stepwise composed model corresponds to the patterns included in the training data. It allows for representation of high uncertain and nonlinear dynamic systems, dependent on local RE production, not substantially reducing the input vector dimensionality leading to model over simplifications as standard AI does. Available angular and frequency time data (e.g., wind direction, humidity, and irradiation cycles) are combined with the amplitudes to solve reduced Partial Differential Equations (PDEs), defined in network nodes, in the periodical complex form. This is a substantial improvement over the previous publication design. The comparative results show better efficiency and reliability of differential learning in representing the modular uncertainty and PDE dynamics of patterns on a day horizon, taking into account recent deep and stochastic learning. A free available C++ parametric software together with the processed meteo-data sets allow additional comparisons with the presented model results.Web of Scienc

Can we break the addiction to fossil energy? : Proceedings of the 7th Biennial International Workshop Advances in Energy Studies

Sponsored by Obra Social "la Caixa", Norwegian Ministry of Petroleum and Energy, Universitat Autònoma de Barcelona and LIPHE4 Scientific Association, Generalitat de Catalunya.From 1998 onwards, every other two years, the Biennial International Workshop "Advances in Energy Studies" (BIWAES) gathers experts in what can be called energy analysis to present and discuss advances, innovations and visions in energy and energy-related environmental and socioeconomic issues and models. Renowned energy experts and ecologists, such as H.T. Odum, James Kay, Charles Hall, Tim Allen, Vaclav Smil, Robert Herendeen, Jan Szargut, Joseph Tainter and Robert Ulanowicz among others, have discussed at the BIWAES the importance of energy in our society and ecosystems and the ways to better analyze and model their complex relationships. Previous editions of BIWAES have focused on energy flows in ecology and economy; analysis of the supply side; the ecological consequences of energy sources exploitation; and the role of renewable energy sources and new energy carriers. The present Book of Proceedings refers to the seventh Edition, which took place in the month of October 2010 in Barcelona and addressed society's addiction to fossil energy

Big Data Analysis application in the renewable energy market: wind power

Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning

Nowadays, weather prediction is based on numerical models of the physics of the atmosphere. These models are usually run multiple times based on randomly perturbed initial conditions. The resulting so-called ensemble forecasts represent distinct scenarios of the future and provide probabilistic projections. However, these forecasts are subject to systematic errors such as biases and they are often unable to quantify the forecast uncertainty adequately. Statistical postprocessing methods aim to exploit structure in past pairs of forecasts and observations to correct these errors when applied to future forecasts. In this thesis, we develop statistical postprocessing methods based on the central paradigm of probabilistic forecasting, that is, to maximize the sharpness subject to calibration. A wide range of statistical and machine learning methods is presented with a focus on novel neural network-based postprocessing techniques. In particular, we analyze the aggregation of distributional forecasts from neural network ensembles and develop statistical postprocessing methods for ensemble forecasts of wind gusts, with a focus on European winter storms