© 2020 The Author(s). A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom. Due to the comprehensive relationship between affecting factors and real-world building electricity consumption, the adoption of multiple hidden layers in the deep neural network (DFNN) algorithm would improve its prediction accuracy. The architecture of a DFNN model mainly refers to its quantity of hidden layers, quantity of neurons in the hidden layers, activation function in each layer and learning process to obtain the connecting weights. The optimal architecture of DFNN model was generally dete...
Proper analysis of building energy performance requires selecting appropriate models for handling co...
To enhance the prediction performance for building energy consumption, this paper presents a modifie...
Abstract: Energy consumption has been increasing steadily due to globalization and industrialization...
A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed fo...
Abstract—Using BP neural network in past to predict the energy consumption of the building resulted ...
Accurate forecast of energy consumption is essential in building energy management. Owing to the var...
The real-world building can be regarded as a comprehensive energy engineering system; its actual ene...
Accurately predicting power consumption is essential to ensure a safe power supply. Various technolo...
The major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genet...
Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessar...
Energy consumption has been increasing steadily due to globalization and industrialization. Studies ...
Summarization: Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers ...
Summarization: In broad terms, Demand Response refers to the operational, regulatory and technical f...
Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate reside...
In this paper, deep learning methods are compared with traditional statistical learning approaches f...
Proper analysis of building energy performance requires selecting appropriate models for handling co...
To enhance the prediction performance for building energy consumption, this paper presents a modifie...
Abstract: Energy consumption has been increasing steadily due to globalization and industrialization...
A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed fo...
Abstract—Using BP neural network in past to predict the energy consumption of the building resulted ...
Accurate forecast of energy consumption is essential in building energy management. Owing to the var...
The real-world building can be regarded as a comprehensive energy engineering system; its actual ene...
Accurately predicting power consumption is essential to ensure a safe power supply. Various technolo...
The major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genet...
Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessar...
Energy consumption has been increasing steadily due to globalization and industrialization. Studies ...
Summarization: Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers ...
Summarization: In broad terms, Demand Response refers to the operational, regulatory and technical f...
Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate reside...
In this paper, deep learning methods are compared with traditional statistical learning approaches f...
Proper analysis of building energy performance requires selecting appropriate models for handling co...
To enhance the prediction performance for building energy consumption, this paper presents a modifie...
Abstract: Energy consumption has been increasing steadily due to globalization and industrialization...