Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in south-eastern Queensland, Australia, for lead-times of 3–12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used

Tendencias recientes en el pronóstico de velocidad de viento para generación eólica

Este documento tiene como objetivo presentar un marco unificado para discutir, resumir y organizar los principales avances en pronóstico de velocidad de viento para generación eólica utilizando un método auditable, ordenado y reproducible. Los principales hallazgos fueron: La mayor parte de los trabajos provienen de China y Estados Unidos, las series de tiempo usadas poseen una longitud de menos de un año, comúnmente el pronóstico es realizado en un rango de 1 hora a 48 horas hacia adelante. Muchos estudios usan solamente modelos autoregresivos (Lineares y no lineares) o en muchos casos una sola variable explicatoria. Usualmente la variable pronosticada es la velocidad de viento u la potencia generada. La revisión muestra una tendencia en la que los autores están experimentando con modelos híbridos para obtener las ventajas de cada método utilizado, también, una tendencia a utilizar métodos clásicos como redes neuronales, máquinas de vectores de soporte y modelos autorregresivosAbstract: This document aims to provide a unified frame for discussing, summarizing and organizing the main advances in wind power forecasting using an auditable, orderly and reproducible method. Our main findings are the following: most of works forecasting time series from China and United States; time series data usually cover information with a length lower than a year of data. Commonly, the forecast is done for 1 to 48 hours ahead. Many studies using only autorregresive models (linear or no linear) or, in many cases, one explanatory variable. Usually, the variables forecasted are speed and power. The review shows a tendency in which the authors are experimenting with hybrid models to obtain the advantages of each method used, also, a trend to use classical methods such as neural networks, Support Vector Machines and autoregressive models.Maestrí