Solar Power Prediction using LTC Models
International Journal of Electrical and Electronics Research, ISSN: 2347-470X, Vol: 10, Issue: 3, Page: 475-480
2022
- 24Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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- Captures24
- Readers24
- 24
Article Description
░ 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.
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