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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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  • ThesisItemOpen Access
    Comparison of various artificial neuron models for very short-term load forecasting
    (G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Joshi, Harsh; Yadav, Abhishek
    Load forecasting has always been a crucial component of the operational and managerial aspect of efficient power system planning. Since there are several factors on which load forecasting depends, it becomes necessary to find out the level of impact these factors put on it. There are several techniques and models which can be used to forecast load on the basis of requirement such as- regression based model, fuzzy time series based model, support vector machine based model, and artificial neuron network based models etc. Artificial neural network (ANN) based models are considered as one of the popular methods for different levels of forecasting, hence used in the study. Data preparation is performed by transforming the historical electric load of Uttarakhand state adopting Max-Min normalization. The prepared data is partitioned into the categories of training and testing data for further application of the conventional and different multiplicative neuron models. The conventional ANN model having eight input nodes and one output neuron was evaluated with different combinations of activation functions. This model achieved the mean-square-error (MSE) of 0.0007. Among various multiplicative neuron models applied for VSTLF, the overall performance of QIFNM is found to be the best. The QIFNM having a single neuron achieved the MSE of 0.0020. As per, Akaike Information Criterion (AIC), all multiplicative neuron models performed better than the MLP based conventional ANN and QIFNM came out to be the best model among all. The performance analysis of these models revealed that a single multiplicative neuron can be used for VSTLF with better performance as per AIC than that of a network of several neurons of the conventional model.