A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model
| Autor: | Sugata Sen Roy, Bhaswati Ganguli, K. Boopathi, A. G. Rangaraj, Sourav Malakar, Amlan Chakrabarti, Saptarsi Goswami |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer engineering. Computer hardware
business.industry Computer science Testbed Photovoltaic system Context (language use) bidirectional features GHI forecasting time series bidirectional GRU Solar energy Renewable energy Domain (software engineering) TK7885-7895 Feature (computer vision) business Representation (mathematics) Algorithm |
| Zdroj: | Machine Learning and Knowledge Extraction, Vol 3, Iss 47, Pp 946-965 (2021) Machine Learning and Knowledge Extraction; Volume 3; Issue 4; Pages: 946-965 |
| ISSN: | 2504-4990 |
| Popis: | Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India. |
| Databáze: | OpenAIRE |
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