scholarly journals Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 204744-204762
Author(s):  
Abdul Salam Shah ◽  
Haidawati Nasir ◽  
Muhammad Fayaz ◽  
Adidah Lajis ◽  
Israr Ullah ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Extreme Learning Machine ◽  
Smart Homes ◽  
Bat Algorithm ◽  
User Preference ◽  
Parameters Selection ◽  
Energy Consumption Optimization ◽  
Learning Machine

Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight

2021 ◽  
pp. 103848
Author(s):  
Mohamad Razwan Abdul Malek ◽  
Nor Azlina Ab Aziz ◽  
Salem Alelyani ◽  
Mohamed Mohana ◽  
Farah Nur Arina Baharudin ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Smart Homes ◽  
Bat Algorithm ◽  
Inertia Weight ◽  
Energy Consumption Optimization

scholarly journals A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 108 ◽  
Author(s):  
Abdul Shah ◽  
Haidawati Nasir ◽  
Muhammad Fayaz ◽  
Adidah Lajis ◽  
Asadullah Shah
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Optimization Algorithms ◽  
Energy Optimization ◽  
Smart Homes ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Comfort Level ◽  
Energy Consumption Optimization ◽  
User Comfort

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

scholarly journals Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3471
Author(s):  
Wei Sun ◽  
Junjian Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Bat Algorithm ◽  
Carbon Price ◽  
Ensemble Empirical Mode Decomposition ◽  
Carbon Market ◽  
Energy Price ◽  
Price Prediction ◽  
Mode Decomposition ◽  
Learning Machine

In response to climate change and environmental issues, many countries have gradually optimized carbon market management and improved the carbon market trading mechanism. Carbon price prediction plays a pivotal role in promoting carbon market management when investors are guided by prediction to conduct rational carbon trading. A novel carbon price prediction methodology is constructed based on ensemble empirical mode decomposition, improved bat algorithm, and extreme learning machine (EEMD-IBA-ELM) in this study. Firstly, the carbon price is decomposed into multiple regular intrinsic mode function (IMF) components by the ensemble empirical mode decomposition, and partial autocorrelation analysis (PACF) is used to find IMF historical data affecting the current value of IMF. Secondly, the improved bat algorithm (IBA) is used to heighten extreme learning machine (ELM) while adaptive parameters are obtained. Finally, EEMD-IBA-ELM was established to predict carbon price. Simultaneously, energy price fluctuation is introduced into the carbon price prediction model. As a consequence, EEMD-IBA-ELM carbon price prediction ability is further improved. In the empirical analysis, the historical carbon price of European Climate Exchange (ECX) and Korea Exchange (KRX) markets are used to examine the effectiveness and stability of the model. Errors of carbon price prediction in ECX and KRX is 2.1982% and 1.1762%, respectively. The results show that the EEMD-IBA-ELM carbon price prediction model can accurately predict carbon price when prediction effect shows strong stability. Furthermore, carbon price prediction accurateness was significantly enhanced by using energy price fluctuation as an influencing factor of carbon price prediction.

scholarly journals Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

2016 ◽  
Vol 16 (6) ◽  
pp. 83-97 ◽  
Author(s):  
Rui-Dong Wang ◽  
Xue-Shan Sun ◽  
Xin Yang ◽  
Haiju Hu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Distributed Energy ◽  
Manufacturing Enterprises ◽  
High Dimension Data ◽  
Equipment Manufacturing ◽  
Neural Network Algorithm ◽  
Learning Machine

Abstract Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.

scholarly journals Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1331 ◽  
Author(s):  
Peng Jiang ◽  
Jun Dong ◽  
Hui Huang
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Power Consumption ◽  
Extreme Learning Machine ◽  
Activation Function ◽  
Forecasting Model ◽  
Fossil Energy ◽  
Mode Decomposition ◽  
Bacterial Foraging Algorithm ◽  
Learning Machine

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

scholarly journals Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

2021 ◽  
Vol 15 ◽  
Author(s):  
Siyuan Lu ◽  
Shuaiqi Liu ◽  
Shui-Hua Wang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Bat Algorithm ◽  
Magnetic Resonance Images ◽  
Optimal Number ◽  
Brain Images ◽  
Detection Approach ◽  
Learning Machine ◽  
The Brain

Aim: Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment.Method: In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leveraged a sliding window to obtain training and testing samples from input brain images. Then, a 13-layer convolutional neural network (CNN) was designed and trained. Finally, we proposed to utilize an extreme learning machine (ELM) to substitute the last several layers in the CNN for detection. We carried out an experiment to decide the optimal number of layers to be substituted. The parameters in ELM were optimized by a heuristic algorithm named bat algorithm. The evaluation of our approach was based on hold-out validation, and the final predictions were generated by averaging the performance of five runs.Results: Through the experiments, we found replacing the last five layers with ELM can get the optimal results.Conclusion: We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection.

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