A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture

SVR, General Noise Functions and Deep Learning. General Noise Deep Models

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de Lectura: 20-01-2023El aprendizaje automático, ML por sus siglas en inglés, es una rama de la inteligencia artifcial que permite construir sistemas que aprendan a resolver una tarea automáticamente a partir de los datos, en el sentido de que no necesitan ser programados explícitamente con las reglas o el método para hacerlo. ML abarca diferentes tipos de problemas; Uno de ellos, la regresión, implica predecir un resultado numérico y será el foco de atención de esta tesis. Entre los modelos ML utilizados para la regresión, las máquinas de vectores soporte o Support Vector Machines, SVM, son uno de los principales algoritmos de eleccón, habitualmente llamado Support Vector Regression, SVR, cuando se aplica a tareas de regresión. Este tipo de modelos generalmente emplea la función de pérdida ϵ−insensitive, lo que implica asumir una distribución concreta en el ruido presente en los datos, pero recientemente se han propuesto funciones de coste de ruido general para SVR. Estas funciones de coste deberían ser más efectivas cuando se aplican a problemas de regresión cuya distribución de ruido subyacente sigue la asumida para esa función de coste particular. Sin embargo, el uso de estas funciones generales, con la disparidad en las propiedades matemáticas como la diferenciabilidad que implica, hace que el método de optimización estándar utilizado en SVR, optimización mínima secuencial o SMO, ya no sea una posibilidad. Además, posiblemente el principal inconveniente de los modelos SVR es que pueden sufrir problemas de escalabilidad al trabajar con datos de gran tamaño, una situación común en la era de los grandes datos. Por otro lado, los modelos de Aprendizaje Profundo o Deep Learning, DL, pueden manejar grandes conjuntos de datos con mayor facilidad, siendo esta una de las razones fundamentales para explicar su reciente popularidad. Finalmente, aunque los modelos SVR se han estudiado a fondo, la construcción de intervalos de error para ellos parece haber recibido menos atención y sigue siendo un problema sin resolver. Esta es una desventaja signifcativa, ya que en muchas aplicaciones que implican resolver un problema de regresión no solo es util una predicción precisa, sino que también un intervalo de confianza asociado a esta predicción puede ser extremadamente valioso. Teniendo en cuenta todos estos factores, esta tesis tiene cuatro objetivos principales: Primero, proponer un marco para entrenar Modelos SVR de ruido general utilizando como método de optimización Naive Online R Minimization Algorithm, NORMA. En segundo lugar, proporcionar un método para construir modelos DL de ruido general que combinen el procesamiento de características altamente no lineales de los modelos DL con el potencial predictivo de usar funciones de pérdida de ruido general, de las cuales la función de pérdida ϵ−insensitive utilizada en SVR es solo un ejemplo particular. Tercero, describir un enfoque directo para construir intervalos de error para SVR u otros modelos de regresión, basado en asumir la hipótesis de que los residuos siguen una función de distribución concreta. Y finalmente, unificar los tres objetivos anteriores en un marco de modelos unico que permita construir modelos profundos de ruido general para la predicción en problemas de regresión con la posibilidad de obtener intervalos de confianza o intervalos de error asociado

Improved EMD-Based Complex Prediction Model for Wind Power Forecasting

As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.© 2020 Institute of Electrical and Electronics Engineersfi=vertaisarvioitu|en=peerReviewed

Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy