No abstract

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

Forest is one of the main sources of living organisms. Its needs start from the human breath to usage of the wood. But due to many reasons, area occupied by the forest is reducing every year. The reasons behind these environmental impacts are natural disasters (forest fires), deforestation activities, and unlawful actions. Forest fire could be creating most serious threat to wild animals and resources of human welfares. The primary phenomenon of the wild fire occurrence is circumstance hotness of the forest. The dry and hot atmosphere caused the fire in the forest. The deforestation and smuggling activities are also tridents to the available forest. The main consideration of this article is to detect wildfires in advance and protect forest resources from social crimes through advanced sensor integration in the IoT (Internet of Things) environment. A smart forest alert monitoring system has been proposed in this article to avoid forest mishap over by automated self-decision-making protective actions such as parameter measures and alert and implementing the harm mitigation actions related to the hot temperature, humidity, smoke, and smuggling of trees. All the sensors work as per the algorithm designed by the specific application of IoT (Internet of Things). The accurate predictions of the forest fire events and ensuring the forest safety have been tested and verified by a conducted case study on the real forest zone environment.