Computer
Haider M. Al-Mashhadi; Hussain Jassim Fahad
Abstract
Electrical energy is one of the most important components of life today where different fields depend on it. The field of electrical energy distribution (electricity network), which transmits electrical energy from sources to consumers, is one of the most important areas that need to be developed and ...
Read More ...
Electrical energy is one of the most important components of life today where different fields depend on it. The field of electrical energy distribution (electricity network), which transmits electrical energy from sources to consumers, is one of the most important areas that need to be developed and improved. In addition to analyzing electrical energy consumption, it needs to forecast consumption and determine consumer behavior in terms of consumption and how to balance supply and demand. The research aims to analyze weather data and find the relation between the weather factors and energy consumption in order to prepare data to use as a suitable data in machine learning model for future use. This model analyzes the building consumption rate for a particular area and takes into account the weather factors that affect electrical energy consumption, where (temperature, dew point, ultraviolet index) are selects based on the correlation confidence and then divided these factors into a set of categories using the K-Means algorithm to show the effect of each factors on the other.
Computer
Hayder I. Mutar; Muna M. Jawad
Abstract
Wireless Sensor Networks (WSNs) have become the most cost- effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often ...
Read More ...
Wireless Sensor Networks (WSNs) have become the most cost- effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life.