Publication:
Improving Net Energy Metering (NEM) Actual Load Prediction Accuracy using an Adaptive Learning Rate LSTM Model for Residential Use Case

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dc.citedby0
dc.contributor.authorKunalan D.en_US
dc.contributor.authorKrishnan P.S.en_US
dc.contributor.authorRamasamy A.K.en_US
dc.contributor.authorPermal N.en_US
dc.contributor.authorid56395450700en_US
dc.contributor.authorid36053261400en_US
dc.contributor.authorid16023154400en_US
dc.contributor.authorid56781496300en_US
dc.date.accessioned2024-10-14T03:17:44Z
dc.date.available2024-10-14T03:17:44Z
dc.date.issued2023
dc.description.abstractAs an effort to promote renewable energy-based power generation, one of Malaysia's initiatives is the net-energy metering (NEM) scheme. One of the shortcomings of residential Photovoltaic (PV) systems under the NEM scheme is that it operates with smart meters only whereby the actual load profiles by the residential consumers remain unknown. Accurate load prediction for NEM consumers is crucial for optimizing energy consumption and effectively managing net metering credits. This study proposes a new model that incorporates an adaptive learning rate and Long Short-Term Memory (LSTM) to predict the solar output power that subsequently predicts the actual load used by the NEM residential consumers. 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 outcome of the proposed model is then compared with other state-of-the-art models like ARIMA and regression methods. It is shown that the proposed model outperforms the traditional forecasting models with a Root Mean Square Error (RMSE) value of 0.1942. � 2023 The Authors, published by EDP Sciences.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2003
dc.identifier.doi10.1051/e3sconf/202343302003
dc.identifier.scopus2-s2.0-85175477129
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85175477129&doi=10.1051%2fe3sconf%2f202343302003&partnerID=40&md5=cb087a49ca4a959cae23c61cf5857bfa
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34039
dc.identifier.volume433
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleE3S Web of Conferences
dc.titleImproving Net Energy Metering (NEM) Actual Load Prediction Accuracy using an Adaptive Learning Rate LSTM Model for Residential Use Caseen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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