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Research Article |

Solar Power Prediction using LTC Models

Author(s) : Anunay Gupta1, Anindya Gupta2, Apoorv Bansal3 and Madan Mohan Tripathi4

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 3, Special Issue on Recent Advancements in the Electrical & Electronics Engineering

Publisher : FOREX Publication

Published : 10 August 2022

e-ISSN : 2347-470X

Page(s) : 475-480

DOI: https://doi.org/10.37391/IJEER.100312

Abstract

Renewable energy production has been increasing at a tremendous rate in the past decades. This increase in production has led to various benefits such as low cost of energy production and making energy production independent of fossil fuels. However, in order to fully reap the benefits of renewable energy and produce energy in an optimum manner, it is essential that we forecast energy production. Historically deep learning-based techniques have been successful in accurately forecasting solar energy production. In this paper we develop an ensemble model that utilizes ordinary differential based neural networks (Liquid Time constant Networks and Recurrent Neural networks) to forecast solar power production 24 hours ahead. Our ensemble is able to achieve superior result with MAPE of 5.70% and an MAE of 1.07 MW.

Keywords: Deep learning, Ensemble, Neural Ordinary differential equations, Solar energy forecasting




Anunay Gupta, Electrical Engineering, DTU, New Delhi, India

Anindya Gupta, Electrical Engineering, DTU, New Delhi, India

Apoorv Bansal, Electrical Engineering, DTU, New Delhi, India

Madan Mohan Tripathi, Electrical Engineering, DTU, New Delhi, India; Email: mmmtripathi@gmail.com

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Anunay Gupta, Anindya Gupta, Apoorv Bansal and Madan Mohan Tripathi (2022), Solar Power Prediction using LTC Models. IJEER 10(3), 475-480. DOI: 10.37391/IJEER.100312.