Climate change can be considered to be the greatest environmental challenge our world is facing today. Along with the need to ensure long-term assurance of energy supply, it imposes an obligation on all of us to consider ways of reducing our carbon footprint and sourcing more of our energy from renewable sources. Wind energy is one such source and forecasting methods for the prediction of wind speed are becoming increasingly significant due to the penetration of wind power as an alternative to conventional energy sources. This paper proposes time series models for short-term prediction of wind speed. The predictions are done for 1 day ahead using different time series models. For each model, these predicted values are compared with the actual values for the next day. Basic exponential smoothing for different duration of data was tested. A hybrid model with decomposition and exponential smoothing is proposed. A multiplicative decomposition is carried out for the measured data. Separate models were developed for seasonal and trend series and then combined to carry out the forecast. The models were tested for different durations of samples and different weather conditions. It is observed from the results that the prediction with decomposition model for 4 months data gave the least error.

中文翻译：

---

时间序列分解模型可准确预测风速

气候变化可以被视为当今世界面临的最大环境挑战。除了需要确保长期保证能源供应外，它还使我们所有人都有义务考虑减少碳足迹并从可再生资源中获取更多能源的方法。风能就是这样的一种来源，并且由于作为传统能源的替代的风能的普及，用于预测风速的预测方法变得越来越重要。本文提出了用于风速短期预测的时间序列模型。使用不同的时间序列模型对未来1天进行了预测。对于每个模型，将这些预测值与第二天的实际值进行比较。测试了不同数据持续时间的基本指数平滑。提出了一种具有分解和指数平滑的混合模型。对测量数据进行乘法分解。针对季节和趋势序列开发了单独的模型，然后将其组合以进行预测。针对不同的采样时间和不同的天气条件对模型进行了测试。从结果可以看出，使用分解模型对4个月数据进行的预测误差最小。