Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …

Current electricity price forecasting models rely on only simple hybridizations of data
preprocessing and optimization methods while ignoring the significance of adaptive data …

As the low-carbon economy continues to advance, New Energy Vehicles (NEVs) have risen
to prominence in the automotive industry. The design and utilization of lithium-ion batteries …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

Over the last decade, China's Peer-to-Peer (P2P) lending industry has been seen as an
important credit source but it has recently suffered from a wave of bankruptcies. Using …

Complex systems constitute components that interact with one another and involve
phenomena which are not always easy to understand in terms of their components and …

An accurate power load prediction in smart grid plays an important role in maintaining the
balance between power supply and demand and thus ensuring the safe and stable …

Accurate wind power generation forecasting can help build a reliable grid; however, the
limited dataset makes accurate forecasting results a challenging work. This study introduces …

Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one
of the major goals of the global effort to protect the ecological environment. However, due to …

In complex financial networks, systemically important nodes usually play crucial roles. Asset
price forecasting is important for describing the evolution of a financial network. Naturally …