Deep belief network-based AR model for nonlinear time series forecasting

Xu, Wenquan; Peng, Hui; Zeng, Xiaoyong; Feng, Zhou; Tian, Xiaoying; Peng, Xiaoyan

doi:10.1016/j.asoc.2019.02.006

Cited by 54 publications

(24 citation statements)

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“…In 2018, it was also shown that the atmospheric variables have effects on both monthly and annual electricity demands (Ahmed et al, 2018). Numerous studies have been published for Australia in 2019 (AL-Musaylh, et al 2019;Singh, et al, 2019;Wu, et al, 2019;Xu et al, 2019;Yang, et al, 2019). A new model has been presented for the effects of temperature fluctuations for Korea on monthly electricity demand (Chang, et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate

Comert

Yıldız

2022

Turkish Journal of Engineering

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Precise electricity demand forecasting has principal significance in the energy production planning of the developing countries. Especially during the last decade, numerous recent methods have been utilized to predict the forthcoming electricity demand in different time resolutions accurately. This contribution presents a novel approach, which improves the forecasting of Turkey’s electricity demand in monthly time resolution. An artificial neural network model has been proposed with appropriate input features. Yearly-based gross demand shows approximately linear increment due to population increase and economic growth, while monthly-based gross demand indicates an oscillation due to the effect of seasonal temperature fluctuations. However, there is no clear linear relation between electricity demand and temperature; for the ideal case, it is the V-shaped curve around balance point temperature. Since temperature levels in each region of the country demonstrate a high variance even in the same time period, weighted average temperature point was calculated with respect to the population weights of the selected regions of Turkey. In order to fit a function for monthly oscillations, a linear function according to weighted average temperature point was created. Unemployment data was added to the training data set as an indicator of economic fluctuations. The mean absolute percentage errors of the model were calculated for training, validation, and testing as 3.77 %, 2.02 %, and 1.95 % respectively.

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Section: Literature Reviewmentioning

confidence: 99%

A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate

Comert

Yıldız

2022

Turkish Journal of Engineering

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“…An AE-LSTM model has been proposed [9]. A DBNbased Auto-Regressive has been proposed for nonlinear TS modeling [90], which provides decent performance. But, the algorithm is fragile when faced with the PV volatile behavior when applied to different locations and not suitable for PVPF.…”

Section: Hybrid Models-basedmentioning

confidence: 99%

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

et al. 2021

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Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data representations to achieve a spectacular performance and high PV forecastability potential compared to classical models. This review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses. Firstly, we draw connections between PVPF and DL approaches and show how this relation might cross-fertilize or extend both directions. Then, fruitful discussions are conducted based on three classes: discriminative learning, generative learning, and deep reinforcement learning. In addition, this review analyzes recent automatic architecture optimization algorithms for DL-based PVPF. Next, the notable DL technologies are thoroughly described. These technologies include federated learning, deep transfer learning, incremental learning, and big data DL. After that, DL methods are taxonomized into deterministic and probabilistic PVPF. Finally, this review concludes with some research gaps and hints about future challenges and research directions in driving the further success of DL techniques to PVPF applications. By compiling this study, we expect to help aspiring stakeholders widen their knowledge of the staggering potential of DL for PVPF.

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“…To improve the accuracy of time series prediction, researchers have proposed various time series forecasting methods, such as simple moving average (SMA) [10] and Holt-exponential smoothing (Holt ES) [11]. Moreover, combining with autoregressive (AR) [12] and moving average (MA), an autoregressive integrated moving average (ARIMA) [13] forecasting model is built with good…”

Section: Introductionmentioning

confidence: 99%

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Zhao

Mo²,

Deng

2020

IEEE Access

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Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity. INDEX TERMS Complex network, time series forecasting, node degree, visibility graph, variation trend.

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Deep belief network-based AR model for nonlinear time series forecasting

Cited by 54 publications

References 50 publications

A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate

A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

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