Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices.

Area of Science:

• Energy harvesting
• Wireless sensor networks
• Renewable energy forecasting

Background:

• Wind energy is a popular power source for wireless sensor networks (WSNs).
• Irregular wind patterns necessitate reliable forecasting for energy consumption management in WSNs.
• Resource-constrained sensor nodes require low-complexity forecasting models to conserve power and computing resources.

Purpose of the Study:

• To develop an efficient, low-complexity forecasting model for short-term wind speed prediction.
• To enable wind-powered sensor nodes to adapt energy consumption to wind energy availability.
• To address the need for accurate wind speed forecasting in WSNs with limited hardware.

Main Methods:

• Development of a novel Autoregressive Integrated Moving Average (ARIMA)-based forecasting model.
• Utilizing real-world datasets for performance evaluation.
• Focusing on short-term wind speed prediction horizons.

Main Results:

• The proposed ARIMA model achieves accurate wind speed predictions.
• The model demonstrates a very low computational burden and memory overhead.
• The model is simple to set up and dynamically adapts to different conditions without retraining.

Conclusions:

• The ARIMA-based model is a suitable choice for wind-powered sensor nodes.
• It provides accurate predictions with minimal resource requirements.
• Its adaptive nature simplifies deployment across various locations and conditions.