An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition
Mid-term electricity load forecasting (LF) plays a critical role in power system planning and operation. To address the issue of error accumulation and transfer during the operation of existing LF models, a novel model called error correction based LF (ECLF) is proposed in this paper, which is designed to provide more accurate and stable LF. Firstly, time series analysis and feature engineering act on the original data to decompose load data into three components and extract relevant features. Then, based on the idea of stacking ensemble, long short-term memory is employed as an error correction module to forecast the components separately, and the forecast results are treated as new features to be fed into extreme gradient boosting for the second-step forecasting. Finally, the component sub-series forecast results are reconstructed to obtain the final LF results. The proposed model is evaluated on real-world electricity load data from two cities in China, and the experimental results demonstrate its superior performance compared to the other benchmark models.
Code (0)
등록된 구현이 없습니다.
Tasks
Feature EngineeringLoad ForecastingTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model
Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly vol…
Decision MakingIISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting
Accurate electricity load forecasting is essential for grid stability, resource optimization, and renewable energy integration. While transformer-based deep learning models like TimeGPT have gained traction in time-serie…
Computational EfficiencyDeep LearningDimensionality ReductionFeature Engineering+3Short Term Load Forecasting Using Deep Neural Networks
Electricity load forecasting plays an important role in the energy planning such as generation and distribution. However, the nonlinearity and dynamic uncertainties in the smart grid environment are the main obstacles in…
Load ForecastingMeta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting
Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification…
ArticlesLoad ForecastingregressionDelta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models
Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consum…