Short-Term Individual Household Load Forecasting Framework Using LSTM Deep Learning Approach

Ibrahim, Nourhan M.; Megahed, A.I.; Abbasy, Nabil H.

doi:10.1109/ismsit52890.2021.9604650

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Combining time series methods with artificial networks algorithms may allow for better results [46]. Due to the nonlinear properties of time series demand profiles, the methods used are not always effective in short-term energy demand forecasting [47]. Time series methods and correlation analysis allowed the identification of indicators useful for more advanced methods, e.g., artificial intelligence.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Energy Consumption in Railway Signal Boxes

Kampik,

Bodzek,

Piaskowy

et al. 2023

Energies

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This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson’s method, Spearman’s method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson’s and Spearman’s coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson’s and Spearman’s coefficients for all analysed objects range from approximately 0.7 to over 0.9.

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Section: Discussionmentioning

confidence: 99%

An Analysis of Energy Consumption in Railway Signal Boxes

Kampik,

Bodzek,

Piaskowy

et al. 2023

Energies

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“…The proposed approach can deal with loads at substations, transformers, and feeders. An LSTM-based model for short-term household load forecasting was developed and tested on two different time series, namely aggregated load at the station level and data from smart meters at the household level [77]. The model outperformed other methods discussed in the paper.…”

Section: Load Forecastingmentioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined.

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“…The RNN algorithm's unique characteristics are passed down to the DL algorithm, allowing the inputs to be considered connected time series. Also, the LSTM cells' intricate structure can solve problems with disappearing and vanishing gradient limitations [33] . Input, cell status, forget, and output gates are the four essential components of the utilized LSTM algorithm.…”

Section: Applying the Proposed Stacked Lstm Snapshot Ensemblementioning

confidence: 99%

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

Alghamdi,

AL–Malaise AL–Ghamdi,

Ragab

2024

Big Data Min. Anal.

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The ability to make accurate energy predictions while considering all related energy factors allows production plants, regulatory bodies, and governments to meet energy demand and assess the effects of energy-saving initiatives. When energy consumption falls within normal parameters, it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy consumption. The objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily interpretable. The proposed model is an implementation of the stacked Long Short-Term Memory (LSTM) snapshot ensemble combined with the Fast Fourier Transform (FFT) and meta-learner. Hebrail and Berard's Individual Household Electric-Power Consumption (IHEPC) dataset incorporated with weather data are used to analyse the model's accuracy with predicting energy consumption.The model is trained, and the results measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination ( ) metrics are 0.020, 0.013, 0.017, and 0.999, respectively. The stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized errors. The results of this study show that prediction accuracy is high, and the model's stability is high as well. The model shows that high levels of accuracy prove accurate predictive ability, and together with high levels of stability, the model has good interpretability, which is not typically accounted for in models. However, this study shows that it can be inferred.

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Short-Term Individual Household Load Forecasting Framework Using LSTM Deep Learning Approach

Cited by 10 publications

References 15 publications

An Analysis of Energy Consumption in Railway Signal Boxes

An Analysis of Energy Consumption in Railway Signal Boxes

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

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