A key driver for develo** more sustainable energy systems is to decrease the effects of
climate change, which could include an increase in the frequency, intensity and duration of …

Renewable energy consumption in agriculture is ascending, catering to the food needs of
the rising population and protecting the environment. Maximizing renewable energy usage …

Greenhouse climate is a highly complex system that contains nonlinearity and
dependencies between each system state. This paper proposes a novel nonlinear model …

A key problem with solar energy is intermittency: solar generators produce only when the
sun is shining, adding to social costs and requiring electricity system operators to reoptimize …

Buildings account for over a third of end energy demand in many countries worldwide.
Modelling this demand accurately marks the first step in producing forecasts that can help …

This paper proposes a novel data-driven robust model predictive control (MPC) framework
for a multi-zone building considering thermal comfort and uncertain weather forecast errors …

Demand-side management (DSM) increases the complexity of forecasting environment,
which makes traditional forecasting methods difficult to meet the firm's need for predictive …

Short-term load forecasting (STLF) plays an irreplaceable role in the efficient management
of electric systems. Particularly in the electricity market and industry, accurate forecasting …

Although predictive control is an effective approach leveraging weather forecast information
to control indoor climate, forecast errors would lead to poor energy management decisions …

The ultimate issue in electricity loads modelling is to improve forecasting accuracy as well as
guarantee a robust prediction result, which will save considerable manual labor material …