Multidimensional Feeding of LSTM Networks for Multivariate Prediction of Energy Time Series

Succetti, Federico; Rosato, Antonello; Araneo, Rodolfo; Panella, Massimo

doi:10.1109/eeeic/icpseurope49358.2020.9160593

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The SOM neural network is an unsupervised network that can do unsupervised data grouping [88,89]. The approach assumes that the input data has certain topological connections or sequences that allow for the realization of the dimensionality reduction mapping from the input n-dimensional space to the output 2-dimensional plane.…”

Section: Hidden-layers Feature Analysis Of Lstm Networkmentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

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Section: Hidden-layers Feature Analysis Of Lstm Networkmentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…1 that shows the complete year. Due to the results not being random, a deep learning artificial neural network method was used which is considered to be good with time series as used by [18]. The dataset were filled with Long Short Term Memory (LSTM), and only considered the data set from "21-August-2008" to "19-November-2009" due to its reputational trend.…”

Section: B Data Processingmentioning

confidence: 99%

A Comparative Study of Methods to Forecast Domestic Energy Consumption Aggregated with Photovoltaic and Heat Pumps System

Rafi

Lee

2021

2021 26th International Conference on Automation and Computing (ICAC)

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Rise in the usage of photovoltaic (PV) system at the residential sector brought challenges for the distribution network operator (DNO) and with a high demand of the Heat Pump (HP) system to fulfil the target of low carbon emission potentially brought far greater trials to predict energy at the domestic network. Prediction is very crucial for electrical distribution companies since their business largely relays on how to make the most out of their energy generation without making it go to waste. This study compares different methods (from machine learning to deep learning) to forecast domestic energy consumption aggregated with HP and PV system. The prediction tool proudly uses large residential energy measured data at a minute frequency for a year combines synthetically with the real measured data of HP and PV system. The forecasting methods is different for various data type, this study allows to compare which one would be more efficient in which type of data set and which one to predicts the finest.

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Time-Series Forecasting of COVID-19 Cases Using Stacked Long Short-Term Memory Networks

Maaliw

Mabunga

Villa

2021

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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Multidimensional Feeding of LSTM Networks for Multivariate Prediction of Energy Time Series

Cited by 4 publications

References 18 publications

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

A Comparative Study of Methods to Forecast Domestic Energy Consumption Aggregated with Photovoltaic and Heat Pumps System

Time-Series Forecasting of COVID-19 Cases Using Stacked Long Short-Term Memory Networks

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