ARTIFICIAL NEURAL NETWORK FOR LONG TERM LOAD FORECASTING IN UTTARAKHAND STATE: A CASE STUDY

2021 ◽  
pp. 55-56
Author(s):  
Rakesh Kumar ◽  
Rakesh Ranjan ◽  
Mukesh Verma
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
New Technologies ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Hydro Power ◽  
Charging Infrastructure ◽  
The Government ◽  
New Infrastructure

Electricity is one of the essential part of our life. With the increase in consumption of resources the demand of electricity is also increased. Uttarakhand as hilly state is approaching towards implementation of new Technologies and Techniques in the area of growth and suistainable development. Due to the implementation of better road infrastructure, tourism connectivity and IoT devices in various projects and inclusion of electric vehicles and their charging infrastructure in Uttarakhand State the demand of electricity has also increased. The Uttarakhand State has planned the establishment of new infrastructure by providing relaxation on various taxes and option of subsidy to investors. The exemption on xed electricity charges is provided to investors in Uttarakhand. The highest part of Electricity Generation is based on Hydro Power in Uttarakhand. By establishment of new infrastructure in Uttarakhand it would be a thrust to load generation companies to produce demanded of electricity on time. In this study the long-term load forecasting from 2022 to 2030 is analysed using Articial Neural Network. The input data is received from Uttarakhand Electricity Regulatory Commission and Uttarakhand Power Corporation Limited. The prediction is based on last 10 years data of historical load, GDP, Population, and past two years data of electric vehicles, and charging infrastructure. In this study, it has reported that by 2030 there would be huge change in infrastructure and most of diesel and petrol vehicles would come on electric vehicles. This study is focused on the Long-Term Load Forecasting in Uttarakhand State where electric vehicles and charging infrastructure load requirement is also calculated. Using Deep Learning Technique in this paper Articial Neural Network is used for forecasting the results. This tool is used to identify the consumption pattern of electricity in Uttarakhand State for further nine years from 2022 to 2030. The Government of Uttarakhand has planned Vision 2030 for the sustainable development in Uttarakhand.

Grey RBF Neural Network Forecasting on LV under the View of Government Responsibility

2012 ◽  
Vol 452-453 ◽  
pp. 700-704
Author(s):  
Feng Rong Zhang ◽  
Annik Magerholm Fet ◽  
Xin Wei Xiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Development Policy ◽  
Rbf Neural Network ◽  
Good Method ◽  
Government Responsibility ◽  
Long Period ◽  
The Government

At present, the domestic research on the scale of macroscopic logistics has yet belonged to the blankness, therefore, this research tries using LV in circulation and LV in stock to measure the logistics volume and forecasting it in a long period. In order to overcome the phenomenon of “floating upward” in long-term period, this paper establish the improved Grey RBF to forecast the LV next 5-10 year in Jilin province of China. The results show that the increased circulation of goods is the main reason leading to increased logistics volume, and the simulation also shows that the improved gray RBF neural network model is a good method for the government to establish the logistics development policy.

scholarly journals Agricultural Crisis and Farmers Suicides in India

2019 ◽  
Vol 8 (11) ◽  
pp. 1576-1580 ◽  
Keyword(s):  
Agricultural Sector ◽  
New Technologies ◽  
Water Levels ◽  
Agricultural Policies ◽  
Review Of The Literature ◽  
Short Term ◽  
Rural People ◽  
Private Money ◽  
The Government

Agricultural sector is the main income for the rural people in India. It plays a significant role in their life. In India, small and marginal farmers account for 70%, according to the 2011 census of the Government of India. These small and marginal farmers took credit from banks and private money lenders. The non-repayment of credit led to an agricultural crisis and farmers’ suicide. This study focused on the reasons that caused such a disaster. The study rests on a review of the literature which was extracted from journals, reports, and newspapers from 2004 to 2019. The review identified the following reasons for the agricultural crisis and farmer’s suicides- poverty, indebtedness, crop failures, distress, lack of awareness on new technologies, inadequate debt, marketing of produce, the high interest of non-institutional credit, and depletion of water levels. The article concluded noting that -the government had to shift its focus from industries to agriculture and shift its agricultural policies from short-term to long- term ones.

scholarly journals Redefining Goods Movement: Building an Ecosystem for the Introduction of Heavy-Duty Battery-Electric Vehicles

2021 ◽  
Vol 12 (3) ◽  
pp. 147
Author(s):  
Dawn Fenton ◽  
Aravind Kailas
Keyword(s):  
Electric Vehicles ◽  
Market Development ◽  
Lead Times ◽  
Heavy Duty ◽  
Charging Infrastructure ◽  
Goods Movement ◽  
Movement Building ◽  
The Government ◽  
The Many ◽  
Development Incentives

This article reviews the Volvo Low-Impact Heavy Green Transport Solution (LIGHTS) project, a multifaceted public–private partnership in Southern California, and provides some early insights and a model for successful fleet adoption of Class 8 battery-electric trucks. This paradigm shift in commercial trucking is emerging, forcing greater interdependence among many stakeholders—fleets, %, truck manufacturers, and policymakers—not currently engaged in the traditional heavy-duty commercial truck market. The many perspectives from this article such as lead times and costs associated with the deployment of charging infrastructure, developing the workforce to support largescale deployments, and the need for market development incentives from the government can be used to inform the programs and policies of California and other states seeking to follow their lead.

Sign in / Sign up

Export Citation Format

Share Document