The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …

Energy consumption forecasting for buildings plays a significant role in building energy
management, conservation and fault diagnosis. Owing to the ease of use and adaptability of …

Electricity is one of the most important end-user energy types in today's world and has an
effective role in development of societies and economies. Stability of electricity supply is …

Energy demand management is particularly important for develo** and emerging
economies. Their energy consumptions increase significantly, depending on their growing …

In electrical power management, load forecasting accuracy is an indispensable factor which
influences the decision making and planning of power companies in the future. Previous …

District heating system (DHS) are the largest energy-consuming component in the building
energy sector. The management and operations of DHS are crucial in supporting energy …

This research is expected to be a reference for PT PLN in delivering information quickly in
predicting the capacity of transformer substations in each region in the industrial area and …

The monthly load curve forecasting problem is discussed, being tackled using artificial
neural networks (ANN). Authors are proposing an enhanced algorithm that includes …

Many models and techniques have been proposed by researchers to improve forecasting
accuracy using fuzzy time series. However, very few studies have tackled problems that …

Fuzzy time series has been implemented for data prediction in the various sectors, such as
education, finance-economic, energy, traffic accident, others. Moreover, many proposed …