Transmission Congestion Management in Deregulated Electricity Market using Multi-Objective Grasshopper Optimization Algorithm

Chatuanramtharnghaka, Benjamin; Deb, Subhasish

doi:10.4108/eai.27-11-2020.167288

Cited by 1 publication

(1 citation statement)

References 25 publications

(29 reference statements)

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“…The schematic diagram of user project interaction is shown in Figure 1 . Conduct one-hot coding on the interaction matrix E and embed the one-hot coding according to a certain dimension to generate e u 1 0 and e i 1 0 , where e u 1 0 represents the initial user feature vector and e i 1 0 represents the initial project feature vector [ 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Wu¹,

Wang²,

Cai³

2022

Computational Intelligence and Neuroscience

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In order to solve the problems of low coverage and accuracy and large mean absolute error and root mean square error when traditional algorithms recommend market management data, this paper proposes an intelligent market management data mining method based on a collaborative recommendation algorithm. According to the preference value of the attribute characteristics of market management data, predict and score the attribute characteristics of market management data; use data mining technology to preprocess the information of market management data, combined with the design of collaborative filtering recommendation algorithm; and realize the collaborative filtering recommendation of market management data. With 50 recommendations, AGCAN improves the accuracy of MovieLens-1M by 43.81%, 5.43%, 1.87%, 0.42%, and 1.67%, respectively, compared with the five benchmark algorithms. For MovieLens-100K, compared with the five benchmark algorithms, AGCAN improves the accuracy by 51.17%, 10.52%, 3.37%, 0.1%, and 0.30%, respectively. Compared with the five benchmark algorithms, Amazon-baby and AGCAN have improved the accuracy by 34.37%, 28.12%, 31.25%, 29.1%, and 3.12%, respectively. The algorithm proposed in this paper uses a graph neural network to mine useful information between users and projects, but it lacks the use of other personalized interest information of users, such as user interest, user purchase time, and so on.

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Section: Methodsmentioning

confidence: 99%