Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

Syed, Dabeeruddin; Abu‐Rub, Haitham; Ghrayeb, Ali; Refaat, Shady S.; Houchati, Mahdi; Bouhali, O.; Bañales, Santiago

doi:10.1109/access.2021.3071654

Cited by 67 publications

(25 citation statements)

References 45 publications

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“…x 1 , x 2 , x 3 ,…, x n is the input of neurons, w 1 , w 2 , w 3 ,…, w n is the input intensity of stimulating neurons, θ is the threshold, and the output is ∑ k =1 n x k w k − θ , where Σ is the accumulator. Only when the output is greater than 0, the neuron is activated; otherwise, it is in an inhibitory state [ 16 ]. F is the activation function.…”

Section: Methodsmentioning

confidence: 99%

Application of Deep Learning Model in Building Energy Consumption Prediction

Wang¹

2022

Computational Intelligence and Neuroscience

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In order to achieve China’s energy conservation and emission reduction goal of peaking carbon dioxide emissions around 2030, it is of great significance. An important means of building energy conservation and emission reduction is the fine management of building energy consumption, which is based on the accurate prediction of building energy consumption, so as to support the optimal management of building operation and achieve the goal of energy conservation and emission reduction. This paper puts forward the evaluation indexes of the results of the building energy consumption prediction model, uses MAPE and RMSE indexes to evaluate the accuracy of the prediction results of the model, and uses the prediction time and input parameter dimensions to evaluate the time cost of the prediction model. Then, using the three building energy consumption prediction models based on machine learning algorithm established above, the prediction of energy consumption of four types of public buildings in different seasons is completed, and the prediction results are evaluated and analyzed. According to the prediction results and the requirements of related work on the accuracy of building energy consumption prediction model, the adaptation relationship between different types of buildings and different machine learning algorithm prediction models is summarized.

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Section: Methodsmentioning

confidence: 99%

Application of Deep Learning Model in Building Energy Consumption Prediction

Wang¹

2022

Computational Intelligence and Neuroscience

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“…A web interface in node js is also added to help as a communication medium between the smart grid and the operator or customer. Smart grid applications such as load forecasting, fault detection, and many others use machine learning capabilities to produce better prediction [68]. Data modeling is discussed in detail in the following section.…”

Section: Data Mining and Analyticsmentioning

confidence: 99%

Big Data Management in Smart Grids: Technologies and Challenges

et al. 2021

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Smart grids are re-engineering the electricity transmission and distribution system throughout the world. It is an amalgam of increased digital information with the electrical power grids. Managing the data generated from the grid efficiently is the key to successful knowledge extraction from the smart grid big data. Most of the scientific advancements are becoming data-driven and becoming an interesting area of research for data scientists. It is challenging the world computationally enough to develop new storage methods and data processing technologies. Managing big data involves data cleaning, integration of varied data sources, and decision-making applications. This paper focuses on the study of big data management and proposes a management process to help manage the data in the grid. Data management tools and techniques have been leveraged in understanding the sources and data types in the grid. The paper emphasizes the limitations of the existing solutions inclined towards applications of the smart grid big data.

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“…5(b). The choice value of = 93 is based on the kmedoids clustering approach proposed in [31]. Hence the value of is chosen as 93 to group data closest to each other, rather than choosing a random value of y.…”

Section: ) Validation Of Execution Timementioning

confidence: 99%

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark

et al. 2021

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Machine learning algorithms have been intensively applied to perform load forecasting to obtain better accuracies as compared to traditional statistical methods. However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. Optimal and effective use of the available computational resources can be attained by maximizing the effective utilization of the cluster nodes. Parallel computing is demanded to allow for optimal resource utilization in dealing with smart grid big data. In this paper, a master-slave parallel computing paradigm is utilized and experimented with for load forecasting in a multi-AMI environment. The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. An efficient resource utilization strategy is proposed for submitting multiple Spark jobs to reduce job completion time. The optimal value of clustering is used in this paper to cluster the data into groups to be able to reduce the computational time additionally. Multiple tree-based machine learning algorithms are tested with parallel computation to evaluate the performance with tunable parameters on a real-world dataset. One thousand distribution transformers' real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.INDEX TERMS Apache spark, concurrent computing, load forecasting, parallel processing, resource management.

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Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

Cited by 67 publications

References 45 publications

Application of Deep Learning Model in Building Energy Consumption Prediction

Application of Deep Learning Model in Building Energy Consumption Prediction

Big Data Management in Smart Grids: Technologies and Challenges

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark

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