Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.

Temporal Variations in Sea of Clouds and Their Relation with Meteorological Factors in Yandang Mountains

Based on data of meteorological elements in the meteorological station in North Yandang Mountains during 1960- 2013,temporal variations in days of sea of clouds over Yandang Mountains in nearly 50 years and their relation with air temperature,precipitation,relative humidity and wind speed were analyzed. The results showed that annual average days of sea of clouds over Yandang Mountains were 164. 92 d,and the maximum and minimum were 215 and 58 d,so there was a big difference between various years. The days of sea of clouds were the most in spring,and average days of sea of clouds（ average days of sea of clouds with low cloud cover ≥80%） were 50. 89 d（ 32. 77 d）,while they were the least in autumn. There was an obvious positive correlation between the days of sea of clouds and relative humidity. Precipitation occurred the day before or on the day when sea of clouds with low cloud cover ≥80% formed. On the day when sea of clouds with low cloud cover ≥80% appeared,average relative humidity was ≥80%,and average wind speed was ≤4. 5 m/s.

Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

Despite the advancement within the last decades in the field of smart grids,energy consumption forecasting utilizing the metrological features is still challenging.This paper proposes a genetic algorithm-based adaptive error curve learning ensemble(GA-ECLE)model.The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach.A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy.This approach combines three models,namely CatBoost(CB),Gradient Boost(GB),and Multilayer Perceptron(MLP).The ensembled CB-GB-MLP model’s inner mechanism consists of generating a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network.A genetic algorithm is used to obtain the optimal features to be used for the model.To prove the proposed model’s effectiveness,we have used a four-phase technique using Jeju island’s real energy consumption data.In the first phase,we have obtained the results by applying the CB-GB-MLP model.In the second phase,we have utilized a GA-ensembled model with optimal features.The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model.The fourth stage is the final stage,where we have applied the GA-ECLE model.We obtained a mean absolute error of 3.05,and a root mean square error of 5.05.Extensive experimental results are provided,demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models.