Publication:
Single Step Multivariate Solar Power Forecasting using Adaptive Learning Rate LSTM Model with Optimized Window Size

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dc.citedby0
dc.contributor.authorKunalan D.en_US
dc.contributor.authorKrishnan P.S.en_US
dc.contributor.authorPermal N.en_US
dc.contributor.authorid56395450700en_US
dc.contributor.authorid36053261400en_US
dc.contributor.authorid56781496300en_US
dc.date.accessioned2024-10-14T03:19:56Z
dc.date.available2024-10-14T03:19:56Z
dc.date.issued2023
dc.description.abstractAccurate photovoltaic (PV) power forecasting is crucial for the successful integration of residential PV systems into the electrical grid. It enables grid operators to optimize grid operations, ensure stability, facilitate market operations and trading, and plan for future system expansion. In this study, we propose a new model that combines an adaptive learning rate Long Short-Term Memory (LSTM) with an optimized window size for improved PV power forecasting. The proposed model is trained and tested using historical time series data of projected PV power and weather conditions, considering the GPS location of the PV system. The model's performance is compared against other commonly used forecasting models, including LSTM, Bi-LSTM, LSTM-Transformer, and CNN-LSTM, for single-step size forecasting, specifically predicting PV power for the next hour. The results demonstrate that the proposed model outperforms all other models in terms of accuracy for the single-step forecasting task. The adaptive learning rate LSTM with optimized window size demonstrates superior performance, indicating its effectiveness in capturing the temporal patterns and dependencies in PV power generation. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/IES59143.2023.10242461
dc.identifier.epage76
dc.identifier.scopus2-s2.0-85173609732
dc.identifier.spage70
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85173609732&doi=10.1109%2fIES59143.2023.10242461&partnerID=40&md5=46003f00227e2019a58c441cb1217c0e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34461
dc.pagecount6
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIES 2023 - International Electronics Symposium: Unlocking the Potential of Immersive Technology to Live a Better Life, Proceeding
dc.subjectAdaptive Learning Rate
dc.subjectLSTM network
dc.subjectOptimized Window Size
dc.subjectPV generation
dc.subjectSolar Irradiation forecasting
dc.subjectCommerce
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectSolar energy
dc.subjectSolar power generation
dc.subjectAdaptive learning rates
dc.subjectLong short-term memory network
dc.subjectMemory network
dc.subjectOptimized window size
dc.subjectOptimized windows
dc.subjectPhotovoltaic power
dc.subjectPhotovoltaics generations
dc.subjectSolar irradiation
dc.subjectSolar irradiation forecasting
dc.subjectWindow Size
dc.subjectLong short-term memory
dc.titleSingle Step Multivariate Solar Power Forecasting using Adaptive Learning Rate LSTM Model with Optimized Window Sizeen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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