Physics Informed Shallow Machine Learning for Wind Speed Prediction

Abstract

The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative datadriven approach based on supervised learning. We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in two central and north west regions of Italy (Abruzzo and Liguria). We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon). Using data from a single location and time horizon we compare systematically several algorithms where we vary the input/output variables, the memory of the input and the linear vs non-linear learning model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance vary with the location. We demonstrate that the presence of a reproducible diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms and show that, when the model is accurately designed, shallow algorithms are competitive with more complex deep architectures.

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

“…Through technological development and fortifying advocacy for ecological protection, wind energy production is more commercially competitive than coal-fired power production [7]. As wind power can be directly linked to the wind speed, the volatility and instability of wind speed would cause instability in wind energy production, which has a massive effect on the power grid [8].…”