An Extreme Gradient Boosting Algorithm for Short-Term Load Forecasting Using Power Grid Big Data

2018 ◽  
pp. 479-490
Author(s):  
Liqiang Ren ◽  
Limin Zhang ◽  
Haipeng Wang ◽  
Qiang Guo
Keyword(s):  
Big Data ◽  
Power Grid ◽  
Load Forecasting ◽  
Gradient Boosting ◽  
Short Term ◽  
Extreme Gradient Boosting ◽  
Short Term Load Forecasting ◽  
Boosting Algorithm

scholarly journals Short-Term Electricity Load Forecasting with Machine Learning

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 50
Author(s):  
Ernesto Aguilar Madrid ◽  
Nuno Antonio
Keyword(s):  
Machine Learning ◽  
Load Forecasting ◽  
Production Costs ◽  
Electricity Production ◽  
Gradient Boosting ◽  
Multiple Sources ◽  
Short Term ◽  
Extreme Gradient Boosting ◽  
Short Term Load Forecasting ◽  
Electricity Load

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

scholarly journals Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Guo ◽  
Kai Zhang ◽  
Xinjie Wei ◽  
Mei Liu
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Prediction Model ◽  
Network Model ◽  
Power Grid ◽  
Load Forecasting ◽  
Model Parameters ◽  
Short Term ◽  
Forecasting Method ◽  
Short Term Load Forecasting

Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.

scholarly journals A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4612 ◽  
Author(s):  
Zhaorui Meng ◽  
Xianze Xu
Keyword(s):  
Power System ◽  
Load Forecasting ◽  
The United States ◽  
Attention Mechanism ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).

Short-Term Load Forecasting With Different Aggregation Strategies

2018 ◽  
Author(s):  
Cong Feng ◽  
Jie Zhang
Keyword(s):  
Load Forecasting ◽  
Information Aggregation ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Short Term ◽  
Calendar Effects ◽  
Network Support ◽  
Short Term Load Forecasting

Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects.

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