In recent times, the power sector has become a focal point of extensive scientific interest, driven by convergence factors, such as mounting global concerns surrounding climate change, persistent increase in electricity prices within wholesale energy market, and surge investments catalyzed technological advancements across diverse sectors. These evolving challenges have necessitated emergence n...

Abstract Electrical load forecasting is of vital importance in intelligent power management and has been a hot spot industrial Internet application field. Due to the complex patterns dynamics data, accurate short-term still challenging task. Currently, many tasks use deep neural networks for forecasting, most recurrent network as basic architecture, including Long Short-Term Memory (LSTM), Sequ...

For the characteristics of fluctuation, periodicity and nonlinearity power load data, this paper proposes a short-term forecasting model based on VMD-Pyraformer-Adan. Firstly, variational modal decomposition (VMD) algorithm is used to modally decompose electric over-zero rate Pearson correlation coefficient are introduced divide components obtain low-frequency, mid-frequency high-frequency part...

Abstract Accuracy and rapidity are the primary objectives of load forecasting, also necessary conditions for ensuring power supply production schedule. However, in actual production, due to variability operating modes interference environment, difficulties such as non-stable high fluctuation prediction. In light this, we propose an adaptive hybrid prediction model based on Discrete Wavelet deco...

With the increase in population and progress of industrialization, rational use energy heating systems has become a research topic for many scholars. The accurate prediction heat load provides us with scientific solution. Due to complexity difficulty forecasting systems, this paper proposes short-term method based on Bayesian algorithm-optimized long- memory network (BO-LSTM). moving average da...

Accurate electricity load forecasting is of crucial importance for power system operation and smart grid energy management. Different factors, such as weather conditions, lagged values, and day types may affect electricity load consumption. We propose to use multiple kernel learning (MKL) for electricity load forecasting, as it provides more flexibilities than traditional kernel methods. Comput...