The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …

Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …

Buildings consume about half of the global electrical energy, and an accurate prediction of
their electricity consumption is crucial for building microgrids' efficient and reliable …

Electrical load forecasting plays a vital role in the operation and planning of power plants for
the utility companies and policy makers to design stable and reliable energy infrastructure …

Accurate heat load forecast is important to operate combined heat and power (CHP)
efficiently. This paper proposes a parallel convolutional neural network (CNN)-long short …

Electric consumption prediction methods are investigated for many reasons, such as
decision-making related to energy efficiency as well as for anticipating demand and the …

Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of
future electricity demand are necessary to ensure power systems' reliable and efficient …

Amongst energy-related CO 2 emissions, electricity is the largest single contributor, and with
the proliferation of electric vehicles and other developments, energy use is expected to …

This paper focuses on the development of shallow and deep neural networks in the form of
multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short …