The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …

The ever-lasting challenge of detecting and mitigating failures in computer networks has
become more essential than ever; especially with the enormous number of smart devices …

With the rapid development of power markets, smart grids and large-scale renewable
energy generation, it is crucial to be able to accurately predict both reactive and active …

This paper aims to develop a load forecasting method for short-term load forecasting, based
on an adaptive two-stage hybrid network with self-organized map (SOM) and support vector …

Very short-term load forecasting predicts the loads 1 h into the future in 5-min steps in a
moving window manner based on real-time data collected. Effective forecasting is important …

Short-term load forecasting has been playing an increasingly important role in electric power
systems. Effective forecasting of the future electricity demand, however, is difficult in view of …

Numerous methods have been proposed for forecasting load for normal days. Modeling of
anomalous load, however, has often been ignored in the research literature. Occurring on …

An accurate load forecast is always important for the power industry and energy players as it
enables stakeholders to make critical decisions. In addition, its importance is further …

In short-term load forecast (STLF), forecasting holiday load is one of the most challenging
problems. Aimed at this problem, a hybrid prediction model based on pattern sequence …

In this paper, a novel ensemble method is proposed to forecast the hourly consumption of
residential electricity. Firstly, variational mode decomposition (VMD) is applied to …