Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

Online Learning for Time Series Prediction

In this paper we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show that our algorithm's performances asymptotically approaches the performance of the best ARMA model in hindsight.Comment: 17 pages, 6 figure

Towards machine learning applied to time series based network traffic forecasting

This TFG will explore some specific use cases of the application of Machine Learning techniques to Software-Define Networks, in particular to overlay protocols such as LISP, VXLAN, etc.The aim of this project is to implement a network traffic forecasting model using time series and improve its performance with machine learning techniques, offering a better prediction based in outlier correction. This is a project developed in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC). Time Series modeling methodology is able to shape a trend and take care of any existing outlier, however it does not cover outlier impact on forecasting. In order to achieve more precision and better confidence intervals, the model combines outlier detection methodology and Artificial Neural Networks to quantify and predict outliers. A study is realized over external data to find out if there is an improvement and its effect on the predictions. Machine learning techniques as Artificial Neural Networks has proven to be an improvement of the current methodology to realize forecasting using Time Series modeling. Future work will be oriented to create an improved standard of this system focused on generalize the model.El objetivo de este proyecto es implementar un modelo de previsión de tráfico de red utilizando series temporales y mejorar su rendimiento con técnicas de aprendizaje automático, generando una mejor predicción basada en la corrección de valores atípicos. Se trata de un proyecto desarrollado en el Departamento de Arquitectura de Computadores (DAC) de la Universidad Politécnica de Cataluña (UPC). La metodología de modelado de series temporales es capaz de predecir una tendencia y hacerse cargo de cualquier valor atípico ya existente, sin embargo, no cubre el impacto de estos sobre la predicción. Con el fin de lograr una mayor precisión y mejores intervalos de confianza, el modelo combina la metodología de detección de valores atípicos y redes neuronales artificiales para cuantificar y predecir los atípicos. Un estudio se realiza sobre datos externos para averiguar si hay una mejora y su efecto sobre las predicciones. Las técnicas de aprendizaje automático, como redes neuronales artificiales, han demostrado ser una mejora de la metodología actual para realizar la predicción utilizando modelos de series de tiempo. El trabajo futuro se orientará para crear un mejor nivel de este sistema se centró en generalizar el modelo.L'objectiu d'aquest projecte és implementar un model de previsió de tràfic de xarxa utilitzant sèries temporals i millorar el seu rendiment amb tècniques d'aprenentatge automàtic, generant una millor predicció basada en la correcció de valors atípics. Es tracta d'un projecte desenvolupat al Departament d'Arquitectura de Computadors (DAC) de la Universitat Politècnica de Catalunya (UPC). La metodologia de modelatge de sèries temporals és capaç de predir una tendència i fer-se càrrec de qualsevol valor atípic ja existent, però, no cobreix l'impacte d'aquests sobre la predicció. Per tal d'aconseguir una major precisió i millors intervals de confiança, el model combina la metodologia de detecció de valors atípics i xarxes neuronals artificials per quantificar i predir els atípics. Un estudi es realitza sobre dades externes per esbrinar si hi ha una millora i el seu efecte sobre les prediccions. Les tècniques d'aprenentatge automàtic, com xarxes neuronals artificials, han demostrat ser una millora de la metodologia actual per a fer predicció utilitzant models de sèries de temps. El treball futur s'orientarà per crear un millor nivell d'aquest sistema es va centrar en generalitzar el model