Work

Short-Term Photovoltaic Power Forecasting Using a Neural Network Dynamic Time Series

Year: 2024

Type: conference-paper

Abstract: Currently, short-term photovoltaic (PV) power forecasting is crucial for optimizing PV power operation in both off-grid and grid-connected systems.This paper compares different PV power forecasting models: Nonlinear Autoregressive (NAR), Nonlinear Input-Output (NIO), and Nonlinear Autoregressive with External Input (NARX) for a PV rooftop 23.1 kWp.The primary input data consists of solar irradiance and temperatures from 1 to 6 hours before the present time.The training process utilizes one year of data (2019) to develop the learning model.Data from January 2020 to April 2020 were used to test the models.This paper reports the optimized time series and number of neurons, with MAPE and R 2 used as accuracy indicators for evaluating the models.The best model, NARX achieved MAPE=6.84% and R 2 =0.9 with a time series delay of 3 hours and 50 neurons.

Source: Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES)

DOI: 10.5109/7323261

Authors: Thatree Mamee, Usa Boonbumrung, Netithorn Ditnin, Patamaporn Sripadungtham, Nitikorn Nanthawirojsiri

Institutions: King Mongkut's University of Technology Thonburi, Kasetsart University

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: hybrid

Funders: National Research Council of Thailand, Electricity Generating Authority of Thailand