Accurate electricity consumption prediction using enhanced long short‐term memory

Chinnaraji, Ragupathi; Ragupathy, Prakash

doi:10.1049/cmu2.12384

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Similarly, in [ 35 ], inspects the potential gains of MAPE. It‘s values help to get accuracy rates, yet it gives no direction about other indispensable factors.…”

Section: Resultsmentioning

confidence: 99%

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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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“…Similarly, in [ 35 ], inspects the potential gains of MAPE. It‘s values help to get accuracy rates, yet it gives no direction about other indispensable factors.…”

Section: Resultsmentioning

confidence: 99%

“…Likewise, in [ 35 ] authors examine the upsides of MAPE only. MAPE values are useful to get precision rates, yet it provides no guidance about other vital variables.…”

Section: Discussionmentioning

confidence: 99%

Multi-region electricity demand prediction with ensemble deep neural networks

et al. 2023

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Short‐term energy forecasting using deep neural networks: Prospects and challenges

Tsegaye,

Sanjeevikumar,

Tjernberg

et al. 2024

The Journal of Engineering

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This study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

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A deep convolutional neural network for predicting electricity consumption at Grey Nuns building in Canada

Elshaboury

Abdelkader

Al-Sakkaf

et al. 2023

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Purpose The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management. Design/methodology/approach This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R2], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models. Findings The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R2, RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively. Originality/value This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

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Accurate electricity consumption prediction using enhanced long short‐term memory

Cited by 10 publications

References 25 publications

Multi-region electricity demand prediction with ensemble deep neural networks

Multi-region electricity demand prediction with ensemble deep neural networks

Short‐term energy forecasting using deep neural networks: Prospects and challenges

A deep convolutional neural network for predicting electricity consumption at Grey Nuns building in Canada

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