Toggle Main Menu Toggle Search

Open Access padlockePrints

Photovoltaic Power Forecasting using LSTM on Limited Dataset

Lookup NU author(s): Tiong Teck Teo, Dr Wai Lok Woo, Dr Thillainathan Logenthiran


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 2018 IEEE. This paper aims to forecast the photovoltaic power, which is beneficial for grid planmng 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.

Publication metadata

Author(s): De V, Teo TT, Woo WL, Logenthiran T

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018

Year of Conference: 2018

Pages: 710-715

Online publication date: 20/09/2018

Acceptance date: 22/05/2018

Publisher: IEEE


DOI: 10.1109/ISGT-Asia.2018.8467934

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538642917