Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

Zhu, Guorong; Sha, Peng; Lao, Yongchang; Su, Qichao; Sun, Qiujie

doi:10.1155/2021/6613604

Cited by 14 publications

(12 citation statements)

References 19 publications

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“…When the result is diverged from the objective result, which is the genuine result, the error is then delivered. To refresh the loads, the network (RNN) is prepared after the error is backpropagated to it [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Multi-region electricity demand prediction with ensemble deep neural networks

et al. 2023

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Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.

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Section: Methodsmentioning

confidence: 99%

Multi-region electricity demand prediction with ensemble deep neural networks

et al. 2023

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“…In order to obtain the optimal value of Equation (11), VMD applies the multiplicative operator alternation method to cyclically update each decomposition signal {u k } and its corresponding center frequency {w k } with the cyclic update of Equations ( 14) and (15).…”

Section: Variable Modal Decompositionmentioning

confidence: 99%

“…Energies 2022, 15, 487 2 of 18 Zhu et al [11] used EMD-Fbprophet-LSTM to predict the daily electricity consumption of enterprises to address the nonstationary nature of electricity consumption data. Semero et al [12] used empirical modal decomposition (EMD) to decompose the short-term load in a microgrid to obtain better prediction results.…”

Section: Introductionmentioning

confidence: 99%

VMD-WSLSTM Load Prediction Model Based on Shapley Values

2022

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Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

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“…(i) Short-term, which covers a time frame of up to one day; it is useful in supply and demand (SD) adjustment [12]. (ii) Medium-term, which covers a time frame from one day up to a year; it is useful in maintenance and outage planning [13].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning for Building Load Forecasting

Patsakos

Vrochidou

Papakostas

2022

Mathematical Problems in Engineering

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Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.

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Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

Cited by 14 publications

References 19 publications

Multi-region electricity demand prediction with ensemble deep neural networks

Multi-region electricity demand prediction with ensemble deep neural networks

VMD-WSLSTM Load Prediction Model Based on Shapley Values

A Survey on Deep Learning for Building Load Forecasting

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