An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition

by   Liping Zhang, et al.

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.


page 1

page 2

page 3

page 4


A Unifying Framework of Attention-based Neural Load Forecasting

Accurate load forecasting is critical for reliable and efficient plannin...

Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Electricity load forecasting is crucial for the power systems' planning ...

A Hybrid Long-Term Load Forecasting Model for Distribution Feeder Peak Demand using LSTM Neural Network

Long Short-Term Memory (LSTM) neural network is an enhanced Recurrent Ne...

A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads

As a powerful tool to improve their efficiency and sustainability, most ...

Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets

Electricity load forecasting enables the grid operators to optimally imp...

Multi-year Long-term Load Forecast for Area Distribution Feeders based on Selective Sequence Learning

Long-term load forecast (LTLF) for area distribution feeders is one of t...

Novel Compositional Data's Grey Model for Structurally Forecasting Arctic Crude Oil Import

The reserve of crude oil in the Arctic area is abundant. Ice melting is ...

Please sign up or login with your details

Forgot password? Click here to reset