A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

Abstract

Large-scale wind power access may cause a series of safety and stability problems. Wind power forecasting (WPF) is beneficial to dispatch in advance. In this paper, a new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering is proposed for short-term wind power forecasting. Based on the similarity among historical days’ weather, k-means clustering algorithm is used to divide the samples into several categories. Additionally, we also create some time features and drop unimportant features through feature engineering. For each category, we make predictions using XGBoost. The results of the proposed model are compared with the back propagation neural network (BPNN) and classification and regression tree (CART), random forests (RF), support vector regression (SVR), and a single XGBoost model. It is shown that the proposed model produces the highest forecasting accuracy among all these models.

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

“…Many different methods (models) can be used to prepare short-term or very shortterm forecasts of wind power generation. Some examples are given as follows: the model of wavelet decomposition and weighted random forest optimized by the niche immune lion algorithm utilized for ultra-short-term wind power forecasts [15]; hybrid empirical mode decomposition and ensemble empirical mode decomposition models (for wind power forecasts) [16]; a neuro fuzzy system with grid partition, a neuro fuzzy system with subtractive clustering, a least square support vector regression, and a regression tree (in the forecasting of hourly wind power) [17]; different approaches for minute-scale forecasts of wind power [18]; the Takagi-Sugeno fuzzy model utilized for ultra-short-term forecasts of wind power [19]; the integration of clustering, two-stage decomposition, parameter optimization, and optimal combination of multiple machine learning approaches (for compound wind power forecasts) [20]; hybrid model using modified long short-term memory for short-term wind power prediction [21]; extreme gradient boosting model with weather similarity analysis and feature engineering (for short-term wind power forecasts) [22]; a model for wind power forecasts utilizing a deep learning approach [23]; hybrid model using data preprocessing strategy and improved extreme learning machine with kernel (for wind power forecasts) [24]; the use of dual-Doppler radar observations of wind speed and direction for five-minute forecasts of wind power [25]; the multi-stage intelligent algorithm which combines the Beveridge-Nelson decomposition approach, the least square support vector machine, and intelligent optimization approach called the Grasshopper Optimization Algorithm (for short-term wind power forecasts) [26]; hybrid model, which combines extreme-point symmetric mode decomposition, extreme learning machine, and PSO algorithm (for short-term wind forecasts) [27]; chicken swarm algorithm optimization support vector machine model for short-term forecasts of wind power [28]; hybrid neural network based on gated recurrent unit with uncertain factors (for ultra-shortterm forecasts of wind power) [29]; discrete time Markov chain models for very short-term wind power forecasts [30].…”

“…The final prediction is the average value from all m single decision trees. This machine learning method is very effective and is recommended in many papers for forecasting tasks [22,60,61]. In comparison with random forest, this method has one additional hyperparameter-learning rate which scales the contribution of each tree [49,59,62,63].…”

Noise Prediction Using Machine Learning with Measurements Analysis

“…For example, a travel time prediction model based on gradient boosting decision tree (GBDT) has been proposed to improve the prediction accuracy of traffic flow [21]. A new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering was proposed for short-term wind power forecasting [22]/ Air quality prediction in smart cities was undertaken using machine learning technologies based on sensor data [23]. This paper presented an innovative gradient boosting decision tree (GBDT) model to explore the joint effects of comprehensive factors on the traffic accident indicators [24].…”

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

“…In [18], the authors propose the use of a modified algorithm based on XGBoost for forecasting wind energy for use in the electrical system. The results of this model are compared with others, based on neural networks (BPNN), regression trees (CART), support vector regression (SVR), and, with a simple XGBoost model, obtaining the best accuracy results in the prediction.…”