Uncovering Dominant Features in Short-term Power Load Forecasting Based on Multi-source Feature

Due to the limitation of data availability, traditional power load forecasting methods focus more on studying the load variation pattern and the influence of only a few factors such as temperature and holidays, which fail to reveal the inner mechanism of load variation. This paper breaks the limitation and collects 80 potential features from astronomy, geography, and society to study the complex nexus between power load variation and influence factors, based on which a short-term power load forecasting method is proposed. Case studies show that, compared with the state-of-the-art methods, the proposed method improves the forecasting accuracy by 33.0% to 34.7%. The forecasting result reveals that geographical features have the most significant impact on improving the load forecasting accuracy, in which temperature is the dominant feature. Astronomical features have more significant influence than social features and features related to the sun play an important role, which are obviously ignored in previous research. Saturday and Monday are the most important social features. Temperature, solar zenith angle, civil twilight duration, and lagged clear sky global horizontal irradiance have a V-shape relationship with power load, indicating that there exist balance points for them. Global horizontal irradiance is negatively related to power load.

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

[2]  Sung-Kwan Joo,et al.  Holiday Load Forecasting Using Fuzzy Polynomial Regression With Weather Feature Selection and Adjustment , 2012, IEEE Transactions on Power Systems.

[3]  Eduard Muljadi,et al.  A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization , 2018, IEEE Transactions on Smart Grid.

[4]  Hyojoo Son,et al.  Short-term forecasting of electricity demand for the residential sector using weather and social variables , 2017 .

[5]  Dan Wang,et al.  Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting , 2019, International Journal of Electrical Power & Energy Systems.

[6]  Pu Wang,et al.  Electric load forecasting with recency effect: A big data approach , 2016 .

[7]  J. Prescott,et al.  The lag of temperature behind solar radiation , 1951 .

[8]  H. Chipman,et al.  BART: Bayesian Additive Regression Trees , 2008, 0806.3286.

[9]  Tao Hong,et al.  Relative Humidity for Load Forecasting Models , 2018, IEEE Transactions on Smart Grid.

[10]  Tao Hong,et al.  Load forecasting using 24 solar terms , 2018 .

[11]  Shanlin Yang,et al.  A deep learning model for short-term power load and probability density forecasting , 2018, Energy.

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

[13]  Adam Kapelner,et al.  bartMachine: Machine Learning with Bayesian Additive Regression Trees , 2013, 1312.2171.

[14]  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.

[15]  Bin Wang,et al.  ELITE: Ensemble of Optimal Input-Pruned Neural Networks Using TRUST-TECH , 2011, IEEE Transactions on Neural Networks.

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

[17]  Julián Moral-Carcedo,et al.  Integrating long-term economic scenarios into peak load forecasting: An application to Spain , 2017 .

[18]  Pan ZENG,et al.  A learning framework based on weighted knowledge transfer for holiday load forecasting , 2019 .

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

[20]  Helge Langseth,et al.  Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning , 2015, IEEE Transactions on Smart Grid.

[21]  Chongqing Kang,et al.  An Ensemble Forecasting Method for the Aggregated Load With Subprofiles , 2018, IEEE Transactions on Smart Grid.