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Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper's model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting. [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

• A hybrid short-term load forecasting method is proposed. • A data preprocessing module is conducted based on de-noising technique and fuzzy time series. • To balance the forecasting accuracy and stability, a multi-objective optimization algorithm is applied to optimize the BPNN model. 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. [ABSTRACT FROM AUTHOR]