Photovoltaic Power Forecasting using LSTM on Limited Dataset

De, Vidisha, Teo, Tiong Teck, Woo, Wai Lok and Logenthiran, T. (2018) Photovoltaic Power Forecasting using LSTM on Limited Dataset. In: ISGT Asia 2018 - International Conference on Innovative Smart Grid Technologies, 22nd - 25th May 2018, Singapore.

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This paper aims to forecast the photovoltaic power, which is beneficial for grid planning which aids in anticipating and prediction in the event of a shortage. Forecasting of photovoltaic power using Recurrent Neural Network (RNN) is the focus of this paper. The training algorithm used for RNN is Long Short-Term Memory (LSTM). To ensure that the amount of energy being harvested from the solar panel is sufficient to match the demand, forecasting its output power will aid to anticipate and predict at times of a shortage. However, due to the intermittent nature of photovoltaic, accurate photovoltaic power forecasting can be difficult. Therefore, the purpose of this paper is to use long short-term memory to obtain an accurate forecast of photovoltaic power. In this paper, Python with Keras is used to implement the neural network model. Simulation studies were carried out on the developed model and simulation results show that the proposed model can forecast photovoltaic power with high accuracy.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Energy management system, LSTM, Photovoltaic, Recurrent neural network, Renewable energy resources Introduction, forecasting
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 29 Mar 2019 14:33
Last Modified: 10 Oct 2019 20:48

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