Investigation of Data Size Variability in Wind Speed Prediction Using AI Algorithms

Abstract

Electricity generation from burning fossil fuel is one of the major contributors to global warming. Renewable energy sources are a viable alternative to produce electrical energy and to reduce the emission from power industry. They have unlocked opportunities for consumers to produce electricity locally and use it on-site that reduces dependency on centralized generation. Despite the widespread availability, one of the major challenges is to understand their characteristics in a more informative way. Wind energy is highly dependent on the intermittent wind speed profile. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy and model comparison for different sizes of data set. The results show, the most effective algorithm varies based on the data size.

Keywords:

• Convolutional neural networks
• deep learning
• long short-term memory (LSTM)
• wind speed prediction

Additional information

Funding

This work was supported by the National Science Foundation (NSF) grants #1900462, #1505509 and #2011927, DoD grants #W911NF1810475 and #W911NF2010274, and the NIH grant #1R25AG067896-01.