An Adaptive and Parallel Forecasting Strategy for Short-Term Power Load Based on Second Learning of Error Trend

Modeling an accurate forecasting model for short-term load is still challenging due to the diverse causes of load changing and lack of information on many of these causes. In this paper, error trend is used to reveal the trend effect caused by unknown load affecting factors and proposed adaptive second learning of error trend (A-SLET) to self-adapt the trend effect. Furthermore, the training set is classified based on balance point temperature and then parallelly trained and tested adaptive forecaster for hot days and adaptive forecaster for cold days with proper data. Combining A-SLET with parallel forecasting and training set classification, Adaptive and Parallel forecasting strategy based on Second Learning of Error Trend (AP-SLET) is proposed. The work studied two distinct load patterns, one in the USA and the other in Australia. Considering the yearly forecasting horizon, MAPE of the adaptive and parallel forecasting strategy is 1.87%-4.04% for ME-Maine of New England and 2.81%-4.41% for New South Wales. Compared to the state-of-art forecasting methods, MAPE of the adaptive and parallel forecasting strategy is reduced by 17.03%-33.33%, RMSE and MAE are reduced by 34.05% and 35.38% respectively. The experimental results demonstrate the proposed strategy can transform unknown and unavailable load affecting factors into known forecasting features and then adapt it to improve forecasting performance. The proposed strategy is also forecaster independent and equally applicable to almost all load scenarios regardless of geographical and seasonal differences.

[1]  M. Karimi,et al.  Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method , 2018 .

[2]  R. Nateghi,et al.  Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region , 2019, Energy.

[3]  S. Barghinia,et al.  A combination method for short term load forecasting used in Iran electricity market by NeuroFuzzy, Bayesian and finding similar days methods , 2008, 2008 5th International Conference on the European Electricity Market.

[4]  Huiru Zhao,et al.  An optimized grey model for annual power load forecasting , 2016 .

[5]  Zhile Yang,et al.  Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches , 2019, Applied Sciences.

[6]  Kashem M. Muttaqi,et al.  Load forecasting under changing climatic conditions for the city of Sydney, Australia , 2018 .

[7]  Tao Hong,et al.  Improving short term load forecast accuracy via combining sister forecasts , 2016 .

[8]  Jianzhou Wang,et al.  Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system , 2018 .

[9]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[10]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[11]  Min Jin,et al.  Holographic Ensemble Forecasting Method for Short-Term Power Load , 2019, IEEE Transactions on Smart Grid.

[12]  Gang Liu,et al.  Modeling of district load forecasting for distributed energy system , 2017 .

[13]  Xiaohui Yang,et al.  Ensemble Residual Networks for Short-Term Load Forecasting , 2020, IEEE Access.

[14]  Canbing Li,et al.  Dynamic Similar Sub-Series Selection Method for Time Series Forecasting , 2018, IEEE Access.

[15]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[16]  Bohdan Pavlyshenko,et al.  Using Stacking Approaches for Machine Learning Models , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[17]  Grzegorz Dudek,et al.  Artificial Immune System With Local Feature Selection for Short-Term Load Forecasting , 2017, IEEE Transactions on Evolutionary Computation.

[18]  Lachlan L. H. Andrew,et al.  Short-term residential load forecasting: Impact of calendar effects and forecast granularity , 2017 .

[19]  Chen Wang,et al.  A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting , 2016 .

[20]  Tomonobu Senjyu,et al.  A neural network based several-hour-ahead electric load forecasting using similar days approach , 2006 .

[21]  Song Li,et al.  A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection , 2016, IEEE Transactions on Power Systems.

[22]  Ying Chen,et al.  Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks , 2010, IEEE Transactions on Power Systems.

[23]  Jianhua Zhang,et al.  A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids , 2014 .

[24]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[25]  Weilin Li,et al.  Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings , 2017 .

[26]  M. Hadi Amini,et al.  A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon , 2017 .

[27]  Adel Akbarimajd,et al.  Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting , 2018, Energy.

[28]  Michael Negnevitsky,et al.  An Effort to Optimize Similar Days Parameters for ANN-Based Electricity Price Forecasting , 2008, IEEE Transactions on Industry Applications.

[29]  Song Li,et al.  An ensemble approach for short-term load forecasting by extreme learning machine , 2016 .

[30]  Mayur Barman,et al.  A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .

[31]  Priyanka Singh,et al.  Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem , 2018 .

[32]  Xiang Zhou,et al.  Short-term power load forecasting using grey correlation contest modeling , 2012, Expert Syst. Appl..

[33]  Pan Zeng,et al.  Peak load forecasting based on multi-source data and day-to-day topological network , 2018 .

[34]  Xiong Luo,et al.  Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy , 2018, IEEE Transactions on Industrial Informatics.

[35]  Igor Škrjanc,et al.  Short-Term Load Forecasting by Separating Daily Profiles and Using a Single Fuzzy Model Across the Entire Domain , 2018, IEEE Transactions on Industrial Electronics.

[36]  Yonggang Wu,et al.  Short-term Load Forecasting Using Improved Similar Days Method , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.