The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

Sardianos, Christos; Varlamis, Iraklis; Chronis, Christos; Dimitrakopoulos, George; Alsalemi, Abdullah; Himeur, Yassine; Bensaali, Fayçal; Amira, Abbes

doi:10.1002/int.22314

Cited by 80 publications

(37 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning models like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and its variants are used for predicting severe weather events accurately which can help the energy sector to improve the accuracy of energy forecasting [31]. The economic savings prospect and the ecological saving prospect are analyzed for increasing user trust and for increasing the acceptance of recommendations to drive energy efficiency [32]. The various optimization problems and the improved strategies for solving the optimization problems can be done using big data and deep learning models.…”

Section: Share In Primary Energymentioning

confidence: 99%

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

Rajamoorthy¹,

Saraswathi²,

Devaraj³

et al. 2022

Sustainability

View full text Add to dashboard Cite

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

show abstract

Section: Share In Primary Energymentioning

confidence: 99%

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

Rajamoorthy¹,

Saraswathi²,

Devaraj³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…It can also improve the interpretability of neural network. 31 Therefore, reasonable use of attention mechanism is helpful to improve the performance of the model. In connection with the next POI category recommendation, it can be understood that the historical check-in records have different impacts on users' next check-in.…”

Section: Attention Modelmentioning

confidence: 99%

“…It is easy to encounter attention model in different types of tasks. It can also improve the interpretability of neural network 31 . Therefore, reasonable use of attention mechanism is helpful to improve the performance of the model.…”

Section: Preliminaries To This Studymentioning

confidence: 99%

An attention‐based category‐aware GRU model for the next POI recommendation

et al. 2021

View full text Add to dashboard Cite

With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior.Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network.Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based categoryaware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.

show abstract

“…Besides the above quality criteria, explainability further impacts the persuasiveness of recommendations. 40 In this study, we mainly discuss four focal criteria-accuracy, diversity, novelty, and tendency.…”

Section: Objective Functionsmentioning

confidence: 99%

Multicriteria recommendation based on bacterial foraging optimization

Geng

Wang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously.Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

show abstract

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

Cited by 80 publications

References 33 publications

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

An attention‐based category‐aware GRU model for the next POI recommendation

Multicriteria recommendation based on bacterial foraging optimization

Contact Info

Product

Resources

About