Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting

Qiu, Xueheng; Ren, Yongmao; Suganthan, Ponnuthurai Nagaratnam; Amaratunga, G.A.J.

doi:10.1016/j.asoc.2017.01.015

Cited by 389 publications

(199 citation statements)

References 36 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…31,32 These studies will help to adjust large-scale PV systems and future projects. Many vital studies [35][36][37][38][39] have confirmed that deep learning algorithms are excellent in predicting such topics. A 1.4-kW GCPV will be evaluated in Sohar in the north part of Oman.…”

Section: Introductionmentioning

confidence: 97%

“…This approach helps to reduce the operational costs of these power plants. Many vital studies [35][36][37][38][39] have confirmed that deep learning algorithms are excellent in predicting such topics. Unfortunately, to date, the application of a deep learning approach to predicting the outcomes of PV stations connected to the grid has only been narrowly applied.…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

View full text Add to dashboard Cite

Summary This article evaluates a 1.4‐kW building integrated grid‐connected photovoltaic plant. The PV plant was installed in the Faculty of Engineering solar energy lab, Sohar University, Oman, and the system data have been collected for a year from July 2017 to June 2018. The grid‐connected system was evaluated in terms of power, energy, specific yield, capacity factor, and cost of energy, and payback period. The measured diffuse and global solar irradiations are 3289 and 6182 Wh/m2, respectively. Four predictive models (TLRN, FRNN‐1, FRNN‐2, and FRNN‐3) using deep learning approach based on RNN and TLRN were proposed to predict the PV current performance through data input of temperature (T) and solar irradiance (G). The experiment results found that the highest energy production, array, reference, and final yields are 245.8 kWh, 3.43 to 5.65 kWh/kWp‐day, 4.61 to 7.33 kWh/kWp‐day, and 3.24 to 4.82 kWh/kWp‐day, respectively. Meanwhile, CF, CoE, and PBP were found to be 21.7%, 0.045 USD/kWh and 11.17 years, respectively. The highest performance for prediction models were found for FRNN‐2 and FRNN‐3 due to they exhibit lower MSE which means being tightly fitted to experiments.

show abstract

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Mean absolute percentage error (MAPE) is the classic evaluation index for load forecasting. The computational formula is given by Equations (16) and (17), where n = 96 represents the dimension of samples and m represents the number of test samples.…”

Section: Comparison Of Results Of Proposed Methodsmentioning

confidence: 99%

Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

et al. 2018

View full text Add to dashboard Cite

Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial intelligent methods have been applied to short-term load forecasting in past research, but most did not consider electricity use characteristics, efficiency, and more influential factors. In this paper, a method for short-term load forecasting with multi-source data using gated recurrent unit neural networks is proposed. The load data of customers are preprocessed by clustering to reduce the interference of electricity use characteristics. The environmental factors including date, weather and temperature are quantified to extend the input of the whole network so that multi-source information is considered. Gated recurrent unit neural networks are used for extracting temporal features with simpler architecture and less convergence time in the hidden layers. The detailed results of the real-world experiments are shown by the forecasting curve and mean absolute percentage error to prove the availability and superiority of the proposed method compared to the current forecasting methods.

show abstract

“…To examine the stationarity of the time series, different methods are used. The most commonly used method is Augumented Dickey-Fuller test (ADF) [4], Schwarz [9], Akaike [1] or Hannan & Quinn [5]. These characteristics need to be identified, removed, or modeled before further analysis can proceed.…”

Section: Stationarity Of the Time Seriesmentioning

confidence: 99%

“…Many works use the elements of the decomposition of the time series to predict future values. In the work [9] an ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep learning approach is presented. The load demand series were first decomposed and few algorithms were used like Intrinsic Mode Functions (IMFs) and Deep Belief Network (DBN).…”

Section: Introductionmentioning

confidence: 99%

The Decomposition Issue of a Time Series in the Forecasting Process

Grzesica¹

2017

International Conference KNOWLEDGE-BASED ORGANIZATION

View full text Add to dashboard Cite

Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting

Cited by 389 publications

References 36 publications

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

The Decomposition Issue of a Time Series in the Forecasting Process

Contact Info

Product

Resources

About