Application of XGboost in Electricity Consumption Prediction

Dong, Danhuang; Fan, Wen; Zhang, Yihong; Qiu, Weitao

doi:10.1109/icetci57876.2023.10176934

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lighter GRU enhances efficiency but slightly sacrifices accuracy compared to the original model. The authors [18] introduced a power consumption forecasting method using the XGBoost algorithm. They applied it to user data from an industrial park and their results showcased the method's remarkable accuracy, adaptability, and suitability for future power grid planning.…”

Section: Literature Reviewmentioning

confidence: 99%

Unraveling Energy Consumption patterns: Insights through Data Analysis and Predictive Modeling

Maleki,

Xie,

Musaddiq

et al. 2024

Preprint

View full text Add to dashboard Cite

Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society's energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R 2 ) value of 0.91 compared to XGBoost value of 0.87.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Unraveling Energy Consumption patterns: Insights through Data Analysis and Predictive Modeling

Maleki,

Xie,

Musaddiq

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Combining Machine Learning techniques and Genetic Algorithm for predicting run times of High Performance Computing jobs

Ramachandran,

Jayalal,

Vasudevan

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model

Xu,

Zheng,

Mei

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Photovoltaic (PV) power fluctuates with weather changes, and traditional forecasting methods typically decompose the power itself to study its characteristics, ignoring the impact of multidimensional weather conditions on the power decomposition. Therefore, this paper proposes a short-term PV power generation method based on MVMD (multivariate variational mode decomposition) feature extraction and the Informer model. First, MIC correlation analysis is used to extract weather features most related to PV power. Next, to more comprehensively describe the relationship between PV power and environmental conditions, MVMD is used for time–frequency synchronous analysis of the PV power time series combined with the highest MIC correlation weather data, obtaining frequency-aligned multivariate intrinsic modes. These modes incorporate multidimensional weather factors into the data-decomposition-based forecasting method. Finally, to enhance the model’s learning capability, the Informer neural network model is employed in the prediction phase. Based on the input PV IMF time series and associated weather mode components, the Informer prediction model is constructed for training and forecasting. The predicted results of different PV IMF modes are then superimposed to obtain the total PV power generation. Experiments show that this method improves PV power generation accuracy, with an MAPE value of 4.31%, demonstrating good robustness. In terms of computational efficiency, the Informer model’s ability to handle long sequences with sparse attention mechanisms reduces training and prediction times by approximately 15%, making it faster than conventional deep learning models.

show abstract

Application of XGboost in Electricity Consumption Prediction

Cited by 3 publications

References 0 publications

Unraveling Energy Consumption patterns: Insights through Data Analysis and Predictive Modeling

Unraveling Energy Consumption patterns: Insights through Data Analysis and Predictive Modeling

Combining Machine Learning techniques and Genetic Algorithm for predicting run times of High Performance Computing jobs

Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model

Contact Info

Product

Resources

About