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 is the lifeblood of modern societies. In the past decades, the world's energy
consumption and associated CO 2 emissions increased rapidly due to the increases in …

Electrical load forecasting plays a vital role in order to achieve the concept of next
generation power system such as smart grid, efficient energy management and better power …

During the past century, energy consumption and associated greenhouse gas emissions
have increased drastically due to a wide variety of factors including both technological and …

Urban energy use modeling is important for understanding and managing energy
performance in cities. However, the existing methods and tools have limitations in …

The electric grid is evolving. Smart grids and demand response systems will increase the
performance of the grid in terms of cost efficiency, resilience and safety. Accurate load …

Accurate electricity demand forecasts that account for impacts of extreme weather events are
needed to inform electric grid operation and utility resource planning, as well as to enhance …

The contribution of buildings to energy consumption (both residential and commercial) is
expected to gradually increase by 2040 in developed countries globally. Energy demand is …

In this paper we present improved short-term load forecasting using bagged neural networks
(BNNs). The BNNs consist of creating multiple sets of data by sampling randomly with …

Meeting the energy goals of the European Union requires new ways of managing energy.
Decentralized energy management, cross-commodity energy production and usage …