A GPU deep learning metaheuristic based model for time series forecasting

Coelho, Igor Machado; Coelho, Vitor Nazário; Luz, Eduardo; Ochi, Luiz Satoru; Guimarães, Frederico Gadelha; Rios, Eyder

doi:10.1016/j.apenergy.2017.01.003

Cited by 115 publications

(47 citation statements)

References 12 publications

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“…Computational intelligence, one of the hottest topic in current academia, becomes a key technology to accurately analyze and forecast time-series data. And, among all these computational intelligence methods, deep learning is in evidence [12]. Deep learning is a newly developed and fast-growing class of machine learning algorithms.…”

Section: B Time-series Forecasting Methodsmentioning

confidence: 99%

When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid

2017

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Electrical load forecasting is still a challenging open problem due to the complex and variable influences, e.g. weather and time. Although, with the recent development of Internet of Things (IoT) and smart meter technology, people have obtained the ability to record relevant information on a large scale, traditional methods struggle in analyzing such complicated relationships for their limited abilities in handling non-linear data. In the paper, we introduce an IoT-based deep learning system to automatically extract features from the captured data, and ultimately, give an accurate estimation of future load value. One significant advantage of our method is the specially designed two-step forecasting scheme, which significantly improves the forecasting precision. Also, the proposed method is able to quantitatively analyze the influences of some major factors, which is of great guiding significance to select attribute combination and deploy on-board sensors for smart grids with vast area, variable climates and social conventions. Simulations demonstrate that our method outperforms some existing approaches, and can be well applied in various situations. Index Terms-Smart grid, Internet of things (IoT), load forecasting, metering infrastructure, big data.

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Section: B Time-series Forecasting Methodsmentioning

confidence: 99%

When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid

2017

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“…The method was used to predict big data times series of Spanish electricity consumption data for 10 years, with a 10‐min sampling rate. In Coelho et al (), a deep learning model was applied for household energy demand forecasting, using a GPU parallel architecture for fast processing and model training. A deep learning forecasting model for multi‐site PV plant connected with a renewable energy management system was introduced in Lee, Lee, and Kim ().…”

Section: Related Workmentioning

confidence: 99%

Big data solar power forecasting based on deep learning and multiple data sources

et al. 2019

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In this paper, we consider the task of predicting the electricity power generated by photovoltaic solar systems for the next day at half‐hourly intervals. We introduce DL, a deep learning approach based on feed‐forward neural networks for big data time series, which decomposes the forecasting problem into several sub‐problems. We conduct a comprehensive evaluation using 2 years of Australian solar data, evaluating accuracy and training time, and comparing the performance of DL with two other advanced methods based on neural networks and pattern sequence similarity. We investigate the use of multiple data sources (solar power and weather data for the previous days, and weather forecast for the next day) and also study the effect of different historical window sizes. The results show that DL produces competitive accuracy results and scales well, and is thus a highly suitable method for big data environments.

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“…Deep learning approaches have emerged as powerful solutions to mitigate these limitations over conventional methods [13]. Most popular deep learning techniques are Multi Layer Perceptron (MLP), Deep Belief Nets (DBN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Auto Encoders (AE) [8,9,10,11,20]. Particularly, RNN based models for predicting air quality have drawn much attention in the recent times.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks

Rao¹,

Devi²,

Ramesh³

2019

IJISA

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The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.Index Terms-Air quality, air pollution, prediction, environment, deep learning, recurrent neural networks, long short term memory.

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A GPU deep learning metaheuristic based model for time series forecasting

Cited by 115 publications

References 12 publications

When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid

When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid

Big data solar power forecasting based on deep learning and multiple data sources

Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks

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