Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Subject: search.filters.subject.Renewable Energy

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A review on development of Smart Grid technology in India and its future perspectives
    (2012) Bala, S.K.; Babu, B.C.; Bala, S.
    India is truculent to meet the electric power demands of a fast expanding economy. Restructuring of the power industry has only increased several challenges for the power system engineers. The proposed vision of introducing viable Smart Grid (SG) at various levels in the Indian power systems has recommended that an advanced automation mechanism needs to be adapted. Smart Grids are introduced to make the grid operation smarter and intelligent. Smart grid operations, upon appropriate deployment can open up new avenues and opportunities with significant financial implications. This paper presents various Smart grid initiatives and implications in the context of power market evolution in India. Various examples of existing structures of automation in India are employed to underscore some of the views presented in this paper. It also reviews the progress made in Smart grid technology research and development since its inception. Attempts are made to highlight the current and future issues involved for the development of Smart Grid technology for future demands in Indian perspective. © 2012 IEEE.
  • No Thumbnail Available
    Item
    Ensemble RDLR Architecture for Short-Term Solar Power Forecasting
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ayappane, H.; Kashyap, Y.
    Given the drastic shift of global sentiment towards renewable energy, it becomes incredibly important to match supply with demand. However the highly variable nature of weather makes it difficult to accurately predict the output of a solar power plant. Through this paper, we will approach this problem by using an ensemble model consisting of both machine learning and neural networks (NN) as base models to forecast the amount of energy that needs to be produced by a solar plant over a short-term time horizon, which in our case will be 0 minute (immediate), 5 minute, 30 minute and 90 minute. Each base model is fine tuned to encourage high diversity and low correlation to improve prediction accuracy. The expected stability or generalization from RF-DNN combined with the memory retention capability of the LSTM network should provide an ideal predictor for time series forecasting of a stochastic process like weather. © 2024 IEEE.