Efficient residential load forecasting using deep learning approach

Mubashar, Rida; Awan, Mazhar Javed; Ahsan, Muhammad; Yasin, Awais; Singh, Vishwa Pratap

doi:10.1504/ijcat.2022.124940

Cited by 18 publications

(5 citation statements)

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“…The study conducted has revealed that the most widely used Deep Learning models in the energy domain for demand forecasting purposes are CNNs, RNNs, LSTM, DQNs, and CRBM and a variation of any of them, a combination of two or more of them, or the combination of any of them with other techniques. Notable are CNN and its variations such as Pyramid-CNN [ 82 , 85 , 88 , 90 , 91 , 94 , 95 , 101 , 106 , 107 , 109 , 115 , 118 , 119 , 123 ], LSTM and its variations such as B-LSTM [ 80 , 82 , 86 , 87 , 88 , 91 , 93 , 94 , 95 , 99 , 100 , 103 , 104 , 106 , 107 , 109 , 110 , 111 , 112 , 113 , 118 , 119 , 122 ], and a combination of both [ 82 , 88 , 91 , 94 , 95 , 106 , 107 , 109 , 118 , 119 ]. Real testbeds with high-quality data are not common, but are necessary to determine the performance of Deep Leaning models.…”

Section: Discussionmentioning

confidence: 99%

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez

Pérez-Juárez

2023

Sensors

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Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

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Section: Discussionmentioning

confidence: 99%

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez

Pérez-Juárez

2023

Sensors

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“…𝑋 𝑖 𝑇+1 = 𝑟 5 𝑋 𝑖 𝑇 − 𝑟 6 𝑋 𝑟 𝑇 + 𝑟 7 𝑋 𝑠𝑡𝑒𝑝 (19) In Eq. ( 19), 𝑟 5 , 𝑟 6 and 𝑟 7 represent the random number lies in [0,1], and 𝑋 𝑠𝑡𝑒𝑝 refers to the whale fall's step size and is formulated by Eq.…”

Section: Figure 2 Architecture Of Cblstm-ae Modelmentioning

confidence: 99%

“…Usability employs these categories of methods for devising the capability of maintenance and grid planning [18]. With machine learning (ML) technologies exploited widely in industries, data-driven techniques are progressively utilized for predicting and analyzing load data like support vector machine (SVM), deep learning (DL), relevance vector machine (RVM) and random forest (RF) methods [19]. Meanwhile, accurate load prediction is required by usability to correctly devise the current grid functions for effective controlling of their resources, This study targets to accurate load forecasting approach [20].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning

Asiri,

Aldehim,

Alotaibi

et al. 2024

IEEE Access

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Load forecasting in Smart Grids (SG) is a major module of current energy management systems, that play a vital role in optimizing resource allocation, improving grid stability, and assisting the combination of renewable energy sources (RES). It contains the predictive of electricity consumption forms over certain time intervals. Load Forecasting remains a stimulating task as load data has exhibited changing patterns because of factors such as weather change and shifts in energy usage behaviour. The beginning of advanced data analytics and machine learning (ML) approaches; particularly deep learning (DL) has mostly enhanced load forecasting accuracy. Deep neural networks (DNNs) namely Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) have achieved popularity for their capability to capture difficult temporal dependencies in load data. This study designs a Short-Load Forecasting scheme using a Hybrid Deep Learning and Beluga Whale Optimization (LFS-HDLBWO) approach. The major intention of the LFS-HDLBWO technique is to predict the load in the SG environment. To accomplish this, the LFS-HDLBWO technique initially uses a Z-score normalization approach for scaling the input dataset. Besides, the LFS-HDLBWO technique makes use of convolutional bidirectional long short-term memory with an autoencoder (CBLSTM-AE) model for load prediction purposes. Finally, the BWO algorithm could be used for optimal hyperparameter selection of the CBLSTM-AE algorithm, which helps to enhance the overall prediction results. A wide-ranging experimental analysis was made to illustrate the better predictive results of the LFS-HDLBWO method. The obtained value demonstrates the outstanding performance of the LFS-HDLBWO system over other existing DL algorithms with a minimum average error rate of 3.43 and 2.26 under FE and Dayton grid datasets, respectively.

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“…In the future, we will use deep learning-based approaches to apply movie recommendations. Various recent studies have been inspired by multimodal-based approaches with efficient deep learning and BigDL framework [45][46][47][48][49][50][51][52]. Moreover, the proposed system can also be applied for books or news recommendations.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

et al. 2021

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In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

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Efficient residential load forecasting using deep learning approach

Cited by 18 publications

References 0 publications

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning

A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

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