A new deep learning restricted boltzmann machine for energy consumption forecasting

Name: A new deep learning restricted boltzmann machine for energy consumption forecasting
Author: Xu, A., Tian, M. W., Firouzi, Behnam, Alattas, K. A., Mohammadzadeh, A., Ghaderpour, E.

İsim	A new deep learning restricted boltzmann machine for energy consumption forecasting
Yazar	Xu, A., Tian, M. W., Firouzi, Behnam, Alattas, K. A., Mohammadzadeh, A., Ghaderpour, E.
Basım Tarihi:	2022-08
Basım Yeri	- MDPI
Konu	Artificial intelligence, Contrastive divergence algorithm, Machine learning, Mid-term load forecasting, Restricted Boltzmann machine
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2071-1050
Kayıt Numarası	a19a6444-24fc-42fa-8ee1-f002c379946b
Tarih	2022-08
Notlar	National Office for Philosophy and Social Sciences
Örnek Metin	A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.
DOI	10.3390/su141610081
Cilt	14

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

A new deep learning restricted boltzmann machine for energy consumption forecasting

Yazar Xu, A., Tian, M. W., Firouzi, Behnam, Alattas, K. A., Mohammadzadeh, A., Ghaderpour, E.

Basım Tarihi 2022-08

Basım Yeri - MDPI

Konu Artificial intelligence, Contrastive divergence algorithm, Machine learning, Mid-term load forecasting, Restricted Boltzmann machine

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2071-1050

Kayıt Numarası a19a6444-24fc-42fa-8ee1-f002c379946b

Tarih 2022-08

Notlar National Office for Philosophy and Social Sciences

Örnek Metin A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.

DOI 10.3390/su141610081

Cilt 14