Search

Electric Load Forecasting (ELF) is one of the challenges being faced by the Power System industry. With the ever-growing consumer demand, power generating companies struggle to manage and provide an uninterrupted power supply to the users. Over the past few decades, the introduction of smart grids and power deregulation has changed load forecasting dynamics. Most of the current research focuses on short-term load forecasting (STLF), involving an hour to a week’s time forecasting. Various techniques are being used for accurately predicting the electric load. However, gold standards are yet to be defined mainly because of the subject's variety, non-linearity, and un-predictive form. In this study critical review of 25 publications has been carried out to find the most efficient method for ELF. The novelty of this study is that comparative and scientific analyses are carried out to find the most proficient techniques for load forecasting. Also, various parameters are combined for comparison in this study after analyzing published reviews on the subject. Artificial Neural Networks (ANN) and Auto-Regressive Moving Average (ARMA) models outperform other methods basing upon statistical analysis, i.e., Mean Absolute Percentage Error (MAPE) and comparative acceptance, in the research community.

The concept of hyper computing (BH) has been introduced to understand how living systems process information. This article presents the BH developments but supports the idea that living beings communicate in structures and not in the mode of signs and symbols. Genetic algorithms are bio-inspired optimization algorithms that simulate the process of the natural evolution of species. They make it possible to manipulate a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of genetic algorithms for statistical machine translation. The bio-inspired communitarian literature proposes a communication model to capture the nature of individuals’ interaction. The research-based on metrics, topology, and algorithms are derived from the bio-inspired communication model’s visual investigation. The evaluation’s assumption is the choice of biologically inspired communication models can influence group performance for a specific task. The communication model was evaluated in two environments. Swarm mission: search for targets communicated among others and avoid opponents. Overall results of the survey Prove that the group agent has the best overall performance when used a bio-inspired communication model to look for specific tasks and avoid compliance with best practices than hostile tasks when using topology models. This study’s main cause is to magnify the group’s mission’s performance by deliberately selecting the bio-inspired communication model.

Vehicle Make and Model Recognition (VMMR) has emerged as a significant element of vision based systems because of its application in access control systems, traffic control and monitoring systems, security systems and surveillance systems, etc. So far a number of techniques have been developed for vehicle recognition. Each technique follows different methodology and classification approaches. The evaluation results highlight the recognition technique with highest accuracy level. In this paper we have pointed out the working of various vehicle make and model recognition techniques and compare these techniques on the basis of methodology, principles, classification approach, classifier and level of recognition. After comparing these factors we concluded that Locally Normalized Harris Corner Strengths (LHNS) performs best as compared to other techniques. LHNS uses Bayes and K-NN classification approaches for vehicle classification. It extracts information from frontal view of vehicles for vehicle make and model recognition.