A fuzzy theory-based machine learning method for workdays and weekends short-term load forecasting

Chen Li

doi:10.1016/j.enbuild.2021.111072

Abstract

Countries around the globe have introduced renewable energies (RE) and minimized the dependency of fossil resources in power systems to address extensive environmental risks. However, such large-scale energy transitions pose a great challenge to power systems due to the volatility of RE. Meanwhile, power demand is increasing over time and it shows temporal characteristics, such as seasonal and peak-valley patterns. Whether the future power system with a larger proportion of RE can meet the surging but fluctuated electricity demand remains problematic. Previous studies on short-term load forecasting focused more on forecasting accuracy than stability. Further, there is a relative paucity of research into temporal patterns. In order to fill in these research gaps, this paper proposes a fuzzy theory-based machine learning model for workdays and weekends short-term load forecasting. Fuzzy time series (FTS) is applied for data mining and back propagation (BP) neural network is used as the main predictor for short-term load forecasting. To exploit the trade-offs between forecasting stability and accuracy, multi-objective optimization is applied to modify the parameters of BP. Moreover, an interval forecasting architecture with several statistical tests is constructed to address forecasting uncertainties. Short-term load data from Victoria in Australia is selected as a case study. Results demonstrate that the proposed method can significantly boost forecasting stability and accuracy, and help strategy making in the field of energy and electricity system management and planning.

Highlights

Transitions on power systems are happening as countries around the globe are introducing RE and minimizing the dependency of fossil resources
There is a relative paucity of research into dynamicity and volatility of short-term electricity in terms of multiply temporal patterns. Important temporal characteristics, such as seasonal and weekday-weekend patterns are not well addressed in current model settings. To fill in these gaps, this paper proposes a hybrid short-term load forecasting model based on data de-noising, the fuzzy time series (FTS) and ANNs with multi-objective optimization
(c) Compared the proposed model ICE-FTS-multi-objective dragonfly algorithm (MODA)-Back propagation (BP) to other hybrid models, it is found that the proposed model has a positive influence on improving the forecasting accuracy and stability

Summary

Introduction

Transitions on power systems are happening as countries around the globe are introducing RE and minimizing the dependency of fossil resources. Classic arithmetic is mainly based on statistical models, such as regression-based models [1,2], Box-Jenkins models [3,4] and Bayesian models [5]. This arithmetic is highly dependent on the quantity of historic data and strict statistical assumptions. It cannot achieve high forecasting accuracy when dealing with non-linear time series

Methods

Findings

Discussion

Conclusion