Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy dema...
Abstract Smart grids and smart homes are getting people’s attention in the modern era of smart citie...
In the current trend of consumption, electricity consumption will become a very high cost for the en...
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The ene...
Energy Consumption has been continuously increasing due to the rapid expansion of high-density citie...
Industrial and building sectors demand efficient smart energy strategies, techniques of optimization...
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The ene...
Our cities face non-stop growth in population and infrastructures and require more energy every day....
Forecasting energy demand has been a critical process in various decision support systems regarding ...
Journal ArticleThis paper presents a recurrent neural network model to make medium-to-long term pred...
The real-world building can be regarded as a comprehensive energy engineering system; its actual ene...
Over the past few years, the number of sensors spread across cities has significantly increased. Thi...
We propose a method for detecting and forecasting events of high energy demand, which are managed at...
Electric power consumption short-term forecasting for individual households is an important and chal...
To combat negative environmental conditions, reduce operating costs, and identify energy savings opp...
The critical transformation of the energy sector demands innovative approaches to ensure the reliabi...
Abstract Smart grids and smart homes are getting people’s attention in the modern era of smart citie...
In the current trend of consumption, electricity consumption will become a very high cost for the en...
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The ene...
Energy Consumption has been continuously increasing due to the rapid expansion of high-density citie...
Industrial and building sectors demand efficient smart energy strategies, techniques of optimization...
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The ene...
Our cities face non-stop growth in population and infrastructures and require more energy every day....
Forecasting energy demand has been a critical process in various decision support systems regarding ...
Journal ArticleThis paper presents a recurrent neural network model to make medium-to-long term pred...
The real-world building can be regarded as a comprehensive energy engineering system; its actual ene...
Over the past few years, the number of sensors spread across cities has significantly increased. Thi...
We propose a method for detecting and forecasting events of high energy demand, which are managed at...
Electric power consumption short-term forecasting for individual households is an important and chal...
To combat negative environmental conditions, reduce operating costs, and identify energy savings opp...
The critical transformation of the energy sector demands innovative approaches to ensure the reliabi...
Abstract Smart grids and smart homes are getting people’s attention in the modern era of smart citie...
In the current trend of consumption, electricity consumption will become a very high cost for the en...
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The ene...