Résumé
In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the "discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers' monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day;thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year's worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
Résumé
More than one year has passed since the outbreak of a new phenomenon in the world, a phenomenon that has affected and transformed all aspects of human life, it is nothing but pandemic of COVID-19. The field of electrical energy is no exception to this rule and has faced many changes and challenges over the 2020. In this paper, by applying artificial intelligence and the integrated clustering model, by k-means technique, combined with the meta-heuristic artificial bee colony (ABC) algorithm a new methodology is presented in order to optimal positioning of the repair crew based on annual data of power grid under situation of COVID-19 to improve the reliability and resiliency of the network due to the importance of electricity for medical purposes, home quarantine, telecommuting, and electronic services. Current research benefits from real interruption data related to year 2020 in Isfahan Province (Iran), reflexing both the huge changes in patterns of power consumption and dispatching as well as novel geographical distribution of blackouts due to COVID pandemic. The temporal distribution of interruptions is very close to the uniform distribution and the geographical distribution of interruptions relative to the density of subscribers had a normal distribution. Accordingly, proposed model is implemented for clustering the spatial data of blackouts recorded during 2020. The number of clusters is equal to the number of repair teams which in this study is considered equal to three. In the next step, the average spatial coordinates of the points of each cluster are calculated, which after reviewing the geographical conditions in the geo-spatial information system (GIS), indicates the optimal point for the deployment of electrical repair crew related to that cluster. The research findings show that after using the optimal points for a month, system average interruption duration index (SAIDI) decreased by an average of 23% compared to the same period of the 2020.
Résumé
More than one year has passed since the outbreak of a new phenomenon in the world, a phenomenon that has affected and transformed all aspects of human life, it is nothing but pandemic of COVID-19. The field of electrical energy is no exception to this rule and has faced many changes and challenges over the 2020. In this paper, by applying artificial intelligence and the integrated clustering model, by k-means technique, combined with the meta-heuristic artificial bee colony (ABC) algorithm a new methodology is presented in order to optimal positioning of the repair crew based on annual data of power grid under situation of COVID-19 to improve the reliability and resiliency of the network due to the importance of electricity for medical purposes, home quarantine, telecommuting, and electronic services. Current research benefits from real interruption data related to year 2020 in Isfahan Province (Iran), reflexing both the huge changes in patterns of power consumption and dispatching as well as novel geographical distribution of blackouts due to COVID pandemic. The temporal distribution of interruptions is very close to the uniform distribution and the geographical distribution of interruptions relative to the density of subscribers had a normal distribution. Accordingly, proposed model is implemented for clustering the spatial data of blackouts recorded during 2020. The number of clusters is equal to the number of repair teams which in this study is considered equal to three. In the next step, the average spatial coordinates of the points of each cluster are calculated, which after reviewing the geographical conditions in the geo-spatial information system (GIS), indicates the optimal point for the deployment of electrical repair crew related to that cluster. The research findings show that after using the optimal points for a month, system average interruption duration index (SAIDI) decreased by an average of 23% compared to the same period of the 2020.
Résumé
• Hybrid ELM models (PSO-ELM, GA-ELM and ABC-ELM) were proposed for estimating ET 0 in different climate zones of China. • PSO-ELM model had the highest accuracy, followed by GA-ELM and ABC-ELM. • Hybrid ELM models outperformed standalone ELM and empirical models in different climate zones. • PSO-ELM model with T max , T min and RH obtained accurate ET 0 estimates in TCZ, SMZ and TMZ. • PSO-ELM model with only T max and T min was better performance on ET 0 estimates in MPZ. Accurate prediction of reference crop evapotranspiration (ET 0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET 0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R 2 = 0.973–0.999) all performed better than the standalone ELM model (R 2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R 2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T max , T min and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R 2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T max and T min data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET 0 with fewer meteorological parameters in different climatic zones of China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)