Multivariate time series prediction by RNN architectures for energy consumption forecasting

Amalou, Ibtissam; Mouhni, Naoual; Abdali, Abdelmounaïm

doi:10.1016/j.egyr.2022.07.139

Cited by 42 publications

(9 citation statements)

References 7 publications

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“…Yang et al [28] employed a nonlinear mapping system to predict wind energy utilization. Amalou et al [29] conducted an evaluation of LSTM, RNN, and GRU in various methods for energy utilization forecasting. Multiple studies have shown that integrating neural networks with multiple layers can effectively enhance the predictive capabilities of individual neural networks, such as the LSTM-RNN [30].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Natarajan,

K. R.,

Wadhwa

et al. 2024

Sustainability

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Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

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Section: Related Workmentioning

confidence: 99%

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Natarajan,

K. R.,

Wadhwa

et al. 2024

Sustainability

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“…Evaluation of this system on the IHEPC and PJM datasets provided good results. Amalou et al [17] investigated a number of deep learning methods, such as RNN, LSTM and GRU to solve the problem of energy consumption management and prediction. The results of this research on the SGSC data set showed that among the mentioned methods, GRU provides the best performance., LSTM, GRU and combined LSTM-GRU learning methods were used by Çetiner [18] to predict energy consumption.…”

Section: Related Workmentioning

confidence: 99%

A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast

Khodadadi,

Riazi,

Yazdani

2024

IJE

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The issue of energy limitation has gained attention as a crisis faced by societies. Buildings play a major role, in energy consumption making it crucial to accurately predict their energy usage. This prediction problem has led researchers to explore machine learning techniques in the field of energy efficiency. In this study we investigated the performance of used machine learning methods like Random Forest (RF) Multi Layer Perceptron (MLP) Linear Regression (LR) and deep learning methods for predicting building energy consumption. The findings revealed that deep learning outperformed methods in solving this problem. To address this we proposed a voting based solution that combines three CNN models with structures and a Deep Neural Network (DNN) method. We applied our proposed method to the WiDS Datathon dataset and achieved promising results. Each of the deep learning methods used in the proposed method provide suitable results and finally, the voting them is done by the averaging. Due to the fact that the proposed method obtains the final result from voting regression models with high accuracy, it is considered a robust model that will be able to provide a suitable prediction against new data.

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“…Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are popular RNN variants commonly used in NLP tasks ( Shewalkar et al, 2019 ; Sherstinsky, 2020 ; Yang et al, 2020 ). They are also well-suited for time series forecasting tasks, such as stock price prediction, weather forecasting, and demand prediction in sales or finance domains, as they are capable of capturing patterns and trends in sequential data ( Alassafi et al, 2022 ; Amalou et al, 2022 ; Bhoj and Bhadoria, 2022 ; Freeborough and van Zyl, 2022 ; Hou et al, 2022 ; Siłka et al, 2022 ). RNNs are also frequently employed in speech recognition systems, speech synthesis (text-to-speech), and speaker identification.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection

Pilcevic,

Djuric Jovicic,

Antonijevic

et al. 2023

Front. Physiol.

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Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results’ interpretation is performed.

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Multivariate time series prediction by RNN architectures for energy consumption forecasting

Cited by 42 publications

References 7 publications

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection

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