A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential Building

Abdulrahman, Mustapha Lawal; Ibrahim, Kabiru Musa; Gital, Abdulsalam Ya’u; Zambuk, Fatima Umar; Ja’afaru, Badamasi; Yakubu, Zahraddeen Ismail; Aliyu, Ibrahim

doi:10.1016/j.procs.2021.10.014

Cited by 22 publications

(3 citation statements)

References 68 publications

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“…In the specific context of LF, DL approaches necessitate the construction of intricate networks, offering distinct advantages over classical techniques, particularly in multi-point scenarios within the load profile. However, it is crucial to acknowledge certain drawbacks, notably in terms of computational complexity and limitations in deterministic point forecasting [37]. Despite these challenges, the appeal of DL approaches for LF remains strong due to their remarkable ability to capture short-and long-term dependencies within input data.…”

Section: Related Workmentioning

confidence: 99%

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Al-Jamimi,

BinMakhashen,

Worku

et al. 2023

Electronics

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Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly in search of advanced machine learning solutions to improve load forecasting. Recently, deep learning (DL) has gained prominence as a significant area of interest in prediction efforts. This paper introduces an innovative approach to electric load forecasting, leveraging advanced DL techniques and making significant contributions to the field of energy management. The hybrid predictive model has been specifically designed to enhance the accuracy of multivariate time series forecasting for electricity consumption within the energy sector. In our comparative analysis, we evaluated the performance of our proposed model against ML-based and state-of-the-art DL models, using a dataset obtained from the Distribution Network Station located in Tetouan City, Morocco. Notably, the proposed model surpassed its counterparts, demonstrating the lowest error in terms of the Root-Mean-Square Error (RMSE). This outcome underscores its superior predictive capability and underscores its potential to advance the accuracy of electricity consumption forecasting.

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Section: Related Workmentioning

confidence: 99%

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Al-Jamimi,

BinMakhashen,

Worku

et al. 2023

Electronics

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“…Machine learning is gaining popularity owing to its ability to enable automated decision-making, reduce human labor, and increase efficiency. It has numerous applications in a variety of industries, including the following: (1) healthcare, where it aids in diagnosis, drug discovery, and medical imaging analysis [17]; (2) marketing, where it aids in customer segmentation, personalization, and recommendation systems [18,19]; (3) transportation, where it aids in autonomous vehicles, route optimization, and traffic prediction [20]; (4) manufacturing, where it aids in predictive maintenance and quality control [21,22]; (5) energy: predictive modeling of renewable energy supplies and demand forecasts [23,24]; and (6) security: facial recognition, intrusion detection, and biometric authentication [25,26].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Air Quality and Meteorology on COVID-19 Cases at Kuala Lumpur and Selangor, Malaysia and Prediction Using Machine Learning

et al. 2023

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Emissions from motor vehicles and industrial sources have contributed to air pollution worldwide. The effect of chronic exposure to air pollution is associated with the severity of the COVID-19 infection. This ecological investigation explored the relationship between meteorological parameters, air pollutants, and COVID-19 cases among residents in Selangor and Kuala Lumpur between 18 March and 1 June in the years 2019 and 2020. The air pollutants considered in this study comprised particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO), whereas wind direction (WD), ambient temperature (AT), relative humidity (RH), solar radiation (SR), and wind speed (WS) were analyzed for meteorological information. On average, air pollutants demonstrated lower concentrations than in 2019 for both locations except PM2.5 in Kuala Lumpur. The cumulative COVID-19 cases were negatively correlated with SR and WS but positively correlated with O3, NO2, RH, PM10, and PM2.5. Overall, RH (r = 0.494; p < 0.001) and PM2.5 (r = −0.396, p < 0.001) were identified as the most significant parameters that correlated positively and negatively with the total cases of COVID-19 in Kuala Lumpur and Selangor, respectively. Boosted Trees (BT) prediction showed that the optimal combination for achieving the lowest Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) and a higher R-squared (R2) correlation between actual and predicted COVID-19 cases was achieved with a learning rate of 0.2, a minimum leaf size of 7, and 30 learners. The model yielded an R2 value of 0.81, a RMSE of 0.44, a MSE of 0.19, and a MAE of 0.35. Using the BT predictive model, the number of COVID-19 cases in Selangor was projected with an R2 value of 0.77. This study aligns with the existing notion of connecting meteorological factors and chronic exposure to airborne pollutants with the incidence of COVID-19. Integrated governance for holistic approaches would be needed for air quality management post-COVID-19 in Malaysia.

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“…An adaptive mechanism that grapples with new load consumption patterns due to concept drift in order to improve the efficiency of DL models -the model is updated automatically according to new energy usage patterns that signal changes. Nonetheless, active/passive tracking of concept drift is prone to various problems-particularly defining a magnitude threshold that would ensure overall good predictions [7][8].…”

Section: Introduction (Gi̇ri̇ş)mentioning

confidence: 99%

A Deep Learning Approach to Real-time Electricity Load Forecasting

AL-HAMİD,

SAVAŞ

2023

Journal of Information Systems and Management Research

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In light of the increasing importance of accurate and real-time electrical demand forecasting, this research presents a deep learning model with the goal of dramatically improving predictive accuracy. Conventional methods of forecasting, such as linear regression, have trouble capturing the complex patterns included in data about electricity usage. Standard machine learning methods are shown to be wanting when compared to the suggested deep Long Short-Term Memory (LSTM) model. Mean Absolute Error (MAE) of 5.454 and Mean Squared Error (MSE) of 18.243 demonstrate the deep LSTM model's proficiency in tackling this problem. The linear regression, on the other hand, achieved a MAE of 47.352 and an MSE of 65.606, which is lower than the proposed model. Because of its greater predictive precision and reliability, the deep LSTM model is a viable option for accurate, real-time prediction of electricity demand.

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A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential Building

Cited by 22 publications

References 68 publications

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

The Impact of Air Quality and Meteorology on COVID-19 Cases at Kuala Lumpur and Selangor, Malaysia and Prediction Using Machine Learning

A Deep Learning Approach to Real-time Electricity Load Forecasting

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