Electric load forecasting has become an important research area for secure operation and management of the modern power systems. In this paper we have proposed a seven support vector machines model for daily peak load demand long range forecasting. One support vector machine for each day of the week is trained on the past data and then used for the forecasting. In tuning process of support vector machines there are few parameters to optimize. We have used genetic algorithm for optimization of these parameters. The proposed model is evaluated on the electric load data used in EUNITE load competition in 2001 arranged by East-Slovakia Power Distribution Company. A better result is found as compare to best result found in the competition.

No abstract

No abstract

Object tracking has gained importance in various applications especially in traffic monitoring, surveillance and security, people tracking, etc. Previous methods of multiobject tracking (MOT) carry out detections and perform object tracking. Although not optimal, these frameworks perform the detection and association of objects with feature extraction separately. In this article, we have proposed a Super Chained Tracker (SCT) model, which is convenient and online and provides better results when compared with existing MOT methods. The proposed model comprises subtasks, object detection, feature manipulation, and using representation learning into one end-to-end solution. It takes adjacent frames as input, converting each frame into bounding boxes’ pairs and chaining them up with Intersection over Union (IoU), Kalman filtering, and bipartite matching. Attention is made by object attention, which is in paired box regression branch, caused by the module of object detection, and a module of ID verification creates identity attention. The detections from these branches are linked together by IoU matching, Kalman filtering, and bipartite matching. This makes our SCT speedy, simple, and effective enough to achieve a Multiobject Tracking Accuracy (MOTA) of 68.4% and Identity F1 (IDF1) of 64.3% on the MOT16 dataset. We have studied existing tracking techniques and analyzed their performance in this work. We have achieved more qualitative and quantitative tracking results than other existing techniques with relatively improved margins.

Multistep ahead time series forecasting has become an important activity in various fields of science and technology due to its usefulness in future events management. Nearest neighbor search is a pattern matching algorithm for forecasting, and the accuracy of the method considerably depends on the similarity of the pattern found in the database with the reference pattern. Original time series is embedded into optimal dimension. The optimal dimension is determined by using autocorrelation function plot. The last vector in the embedded matrix is taken as the reference vector and all the previous vectors as candidate vectors. In nearest neighbor algorithm, the reference vector is matched with all the candidate vectors in terms of Euclidean distance and the best matched pattern is used for forecasting. In this paper, we have proposed a hybrid distance measure to improve the search of the nearest neighbor. The proposed method is based on cross-correlation and Euclidean distance. The candidate patterns are shortlisted by using cross-correlation and then Euclidean distance is used to select the best matched pattern. Moreover, in multistep ahead forecasting, standard nearest neighbor method introduces a bias in the search which results in higher forecasting errors. We have modified the search methodology to remove the bias by ignoring the latest forecasted value during the search of the nearest neighbor in the subsequent iteration. The proposed algorithm is evaluated on two benchmark time series as well as two real life time series.