A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction

Ghimire, Sujan; Nguyen‐Huy, Thong; AL‐Musaylh, Mohanad S.; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo‐Sanz, Sancho

doi:10.1016/j.energy.2023.127430

Cited by 18 publications

(3 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, since physical RC systems can naturally approximate complex dynamical systems, they may provide a unified analogue-computer-like modelling framework for the simulation, control and optimisation of time-critical operations in complex multi-component and multi-scaled electric networks [148][149][150][151]. RC systems in general and physical RC systems in particular have also been efficient in modelling highly nonlinear processes often encountered in energy research [62][63][64][65][66][67][68][69]152] and the predictive control of industrial processes [153]. However, as mentioned previously, RC systems can be readily hybridised with deep learning methods, resulting in a combined approach that benefits from the strengths of deep learning, of ESN and LSM and of the hardware implementation of these algorithms [30].…”

Section: Discussionmentioning

confidence: 99%

“…We also note the remarkable versatility of RC systems, independently of their implementation as a computer program or hardware physical system. In particular, the standard ESN algorithm and its modifications have been used to predict energy consumption and power generation [62][63][64][65][66][67][68][69], thereby extending more traditional modelling approaches such as vector regression models [70]. Thus, the discussion presented below will be of interest to readers with a background in power engineering and adjacent areas, also giving comprehensive information for those seeking to better understand the field of physical reservoir computing and the opportunities that it can bring.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

Maksymov

2023

Energies

View full text Add to dashboard Cite

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

Maksymov

2023

Energies

View full text Add to dashboard Cite

show abstract

“…In order to reduce the empirical risk of sources and targets, early research concentrated on unifying convergence bounds or discovering the latent space shared across data [18]. The application fields of MSDA approaches have been extended by recent research, which includes learning domain invariant features through adversarial learning [19] and integrating moment matching components into deep neural networks [20]. In addition, there are many methods to improve the generalization capability of the model, including using k-domain discriminators [21], merging decision boundaries for sliced Wasserstein distance processing tasks [22], and a new framework for adjusting domain-specific distributions [23].…”

Section: Msdamentioning

confidence: 99%

Dual-weight attention-based multi-source multi-stage alignment domain adaptation for industrial fault diagnosis

Wang,

Chen,

Chen

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Cross-domain fault diagnosis is crucial for industrial applications with various and unknown operating conditions. However, due to the significant differences in the distribution of features in multiple source domains, it may lead to mutual interference of features between different domains and reduce the accuracy of diagnosis, which is a problem not considered by most current researches. In addition, most of the existing methods focus only on the extraction of low-frequency global information and cannot adequately deal with high-frequency local information. Consequently, this paper provides a multi-stage processing integrated dual-weight attention-based multi-source multi-stage aligned domain adaptation (DAMMADA) method. Global fault features that are shared by various subdomains are extracted by three domain-specific feature extractors from various domains. In a local feature extractor, the dual-weight attention module not only uses shared weights to aggregate local information, but it also uses contextual weights to improve local features. In terms of loss handling, multiple pseudo-labels are used to reduce the loss of the local maximum mean discrepancy (LMMD) in order to learn the domain-invariant characteristics after improving the high-frequency and low-frequency information extraction. To modify the classification boundaries, the pseudo-labels' mean square errors (MSE) are combined. Comprehensive experiments were carried out on two platforms for fault diagnosis of SCARA robots and bearings respectively, and the results demonstrated that DAMMADA is superior to other methods in terms of accuracy and its ability to suppress negative migration for cross-domain tasks.

show abstract

Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review

Mathumitha,

Rathika,

Manimala

2024

Artif Intell Rev

View full text Add to dashboard Cite

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy.

show abstract

A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction

Cited by 18 publications

References 79 publications

Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

Dual-weight attention-based multi-source multi-stage alignment domain adaptation for industrial fault diagnosis

Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review

Contact Info

Product

Resources

About