Research on Short-Term Load Forecasting Using XGBoost Based on Similar Days

Liao, Xiaoqun; Cao, Nanlan; Ma, Li; Kang, Xiaofan

doi:10.1109/icitbs.2019.00167

Cited by 65 publications

(26 citation statements)

References 4 publications

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“…The authors of Reference [33] suggest using XGB, including weather variables and historical load, to forecast the hourly weekly load of a power plant. A remark is made on the complexity of the XGB hyperparameter phase.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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“…As indicated by the forecast trial of I15-N interstate traffic information in PeMS information base, the joined model outstripped different models and the prescient exactness of the consolidated model came up at 94.47%. Further, (Liao et al 2019) was considered where-in a load anticipating procedure dependent on XGBoost along with comparative days was proposed. This mechanism was used to break down the basic meteorological laws and everyday types based on the heap load.…”

Section: Related Workmentioning

confidence: 99%

“…The test inferred the normal precision of 99.21% and the normal review rate of 98.5% which eventually demonstrated the whole operation was truly viable and attainable. To comprehend the innovations in XGBoost technology, (Cao et al 2020) threw light upon a momentary traffic stream forecast model. This technique was dependent on best and worst inclination rise such that the analysis results uncovered the predominance of the whole system by contrasting it with the previous anticipation model.…”

Section: Related Workmentioning

confidence: 99%

New York City taxi trip duration prediction using MLP and XGBoost

Poongodi

Malviya

Kumar

et al. 2021

Int J Syst Assur Eng Manag

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New York City taxi rides form the core of the traffic in the city of New York. The many rides taken every day by New Yorkers in the busy city can give us a great idea of traffic times, road blockages, and so on. Predicting the duration of a taxi trip is very important since a user would always like to know precisely how much time it would require of him to travel from one place to another. Given the rising popularity of app-based taxi usage through common vendors like Ola and Uber, competitive pricing has to be offered to ensure users choose them. Prediction of duration and price of trips can help users to plan their trips properly, thus keeping potential margins for traffic congestions. It can also help drivers to determine the correct route which in-turn will take lesser time as accordingly. Moreover, the transparency about pricing and trip duration will help to attract users at times when popular taxi app-based vendor services apply surge fares. Thus in this research study, we used real-time data which customers would provide at the start of a ride, or while booking a ride to predict the duration and fare. This data includes pickup and drop-off point coordinates, the distance of the trip, start time, number of passengers, and a rate code belonging to the different classes of cabs available such that the rate applied is based on a regular or airport basis. Hereafter, we applied XGBoost and Multi-Layer Perceptron models to find out which one of them provides better accuracy and relationships between real-time variables. At last, a comparison of the two mentioned algorithms facilitates us to decide that XGBoost is more fitter and efficient than Multi-Layer Perceptron for taxi trip duration-based predictions.

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“…Liao et al [23] reported in the paper a method of estimating a similar day short load by XGBoost. They identify the most critical factors affecting transportation and prepare the necessary feature map to determine the exact days.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

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Research on Short-Term Load Forecasting Using XGBoost Based on Similar Days

Cited by 65 publications

References 4 publications

Short-Term Electricity Load Forecasting with Machine Learning

Short-Term Electricity Load Forecasting with Machine Learning

New York City taxi trip duration prediction using MLP and XGBoost

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

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