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Hourly Heat Load Prediction for Residential Buildings Based on Multiple Combination Models: A Comparative Study

BUILDINGS 2023年第9期13卷

作者： An, Wenhan Zhu, Xiangyuan Yang, Kaimin Kim, Moon Keun Liu, Jiying Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Oslo Metropolitan Univ Dept Built Environm N-0130 Oslo Norway

The accurate prediction of residential heat load is crucial for effective heating system design, energy management, and cost optimization. In order to further improve the prediction accuracy of the model, this study introduced principal component analysis (PCA), the minimum sum of squares of the combined prediction errors (minSSE), genetic algorithm (GA), and firefly algorithm (FA) into back propagation (BP) and ELMAN neural networks, and established three kinds of combined prediction models. The proposed methodologies are evaluated using real-world data collected from residential buildings over a period of one year. The obtained results of the PCA-BP-ELMAN, FA-ELMAN, and GA-BP models are compared with the neural network before optimization. The experimental results show that the combined prediction models have higher prediction accuracy. The Mean Absolute Percentage Error (MAPE) evaluation indices of the three combined models are distributed between 5.95% and 7.05%. The FA-ELMAN model is the combination model with the highest prediction accuracy, and its MAPE is 5.95%, which is 2.25% lower than the MAPE of an individual neural network. This research contributes to the field by providing a comprehensive and effective framework for residential heat load prediction, which can be valuable for building energy management and optimization.

关键词： heat load prediction combined models principal component analysis genetic algorithm firefly algorithm

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Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization algorithms

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WATER 2019年第4期11卷 742页

作者： Naganna, Sujay Raghavendra Deka, Paresh Chandra Ghorbani, Mohammad Ali Biazar, Seyed Mostafa Al-Ansari, Nadhir Yaseen, Zaher Mundher Shri Madhwa Vadiraja Inst Technol & Management Dept Civil Engn Bantakal 574115 Udupi India Visvesvaraya Technol Univ Belagavi 590018 Karnataka India Natl Inst Technol Karnataka Dept Appl Mech & Hydraul Surathkal 575025 Mangalore India Near East Univ Dept Civil Engn POB 99138Mersin 10 Nicosia North Cyprus Cyprus Univ Tabriz Fac Agr Dept Water Engn Tabriz *** Iran Lulea Univ Technol Civil Environm & Nat Resources Engn S-97187 Lulea Sweden Ton Duc Thang Univ Fac Civil Engn Sustainable Dev Civil Engn Res Grp Ho Chi Minh City Vietnam

Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro-climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP-FFA and MLP-GSA) were authenticated against standard MLP tuned by a Levenberg-Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.

关键词： dew point temperature firefly algorithm gravitational search algorithm humid climate hybrid models nature-inspired optimization semi-arid region

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Differential evolution CapsNet model for QoS routing enhancement in wireless networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第2期35卷 e4146-e4146页

作者： Chandrasekaran, Kumar Selvaraj, Tamizharasi Bannari Amman Inst Technol Dept Elect & Elect Engn Sathyamangalam India RVS Coll Engn & Technol Dept Elect & Commun Engn Coimbatore 641402 Tamil Nadu India

One of the nondeterministic polynomial (NP) hard problem in the area of wireless networks is the quality of service (QoS) routing problem. This has always been a key area wherein research is required to enhance the QoS metrics considered with respect to the considered network model. In this paper, mobile ad hoc network (MANET) model is identified for which an effective QoS routing protocol which meets the set QoS constraints is designed employing the novel variant of differential evolution (DE) model. The ultimate aim of the proposed work is to identify an optimal feasible path for the MANET model with the set objective function criterion being met. A variant of DE capsule net model is built by hybridizing it with the nature-inspired firefly (FF) algorithmic approach. The effectiveness of the proposed technique is achieved by minimizing the set objective function on satisfying the QoS constraints in the MANETs. The developed technique chose better QoS path with respect to the best fitness value than that of the route replay the shortest path algorithm of ad hoc on-demand distance vector (AODV) protocol. The modeled variant DE-FF model achieves better QoS metrics with all the set constrained being met. Simulated results depict the superiority and better performance of the developed DE-FF model than that of the other methods considered for comparison from the existing literature works.

关键词： AODV protocol differential evolution firefly algorithm MANET quality of service routing

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A hybrid optimization-based deep belief neural network for the classification of vegetation area in multi-spectral satellite image

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2020年第4期24卷 363-379页

作者： Gavade, Anil B. Rajpurohit, Vijay S. KLS Gogte Inst Technol Belagavi 590008 Karnataka India

Over the last few decades, multiple advances have been done for the classification of vegetation area through land cover, and land use. However, classification problem is one of the most complicated and contradicting problems that has received considerable attention. Therefore, to tackle this problem, this paper proposes a new firefly-Harmony search based Deep Belief Neural Network method (FHS-DBN) for the classification of land cover, and land use. The segmentation process is done using Bayesian Fuzzy Clustering,and the feature matrix is developed. The feature matrix is given to the proposed FHS-DBN method that distinguishes the land coverfrom the land use in the multispectral satellite images, for analyzing the vegetation area. The proposed FHS-DBN method is designedby training the DBN using the FHS algorithm, which is developed by the combination of firefly algorithm (FA) and Harmony Search (HS) algorithm. The performance of the FHS-DBN model is evaluated using three metrics, such as Accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). From the experimental analysis, it is concludedthat the proposed FHS-DBN model achieves ahigh classification accuracy of 0.9381, 0.9488, 0.9497, and 0.9477 usingIndian Pine, Salinas scene, Pavia Centre and university, and Pavia University scene dataset.

关键词： Classification multi-spectral satellite image firefly algorithm harmony Search algorithm DBN vegetation index

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Distributed Reactive Power Injection-Based Approach for Minimization of Losses in Electrical Networks Considering Heuristic algorithms and Voltage Deviation

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ENERGIES 2023年第19期16卷 6761页

作者： Gilberto Filho Correa, Henrique Pires Vieira, Flavio Henrique Teles Univ Fed Goias UFG Escola Engn Eletr Mecan & Computacao EMC BR-74605010 Goiania Go Brazil

This paper deals with the reduction in electrical losses and the minimization of voltage deviation in electrical power grids. To this end, a novel heuristic-based approach is proposed for controlling reactive power injections along an electrical grid with distributed generation using distributed photovoltaic (PV) generation. The proposed approach consists of applying a heuristic, namely a genetic or firefly algorithm, to solve the loss and voltage deviation optimization problem by controlling PV reactive power injections. The proposed method was tested on a radial 100-bus network with an intense penetration of PV generation. The obtained results indicate that the proposed method consistently yields superior grid performance when compared to algorithms previously proposed in the literature. In fact, it led to a reduction in the active power supplied by the reference bus to the grid, thereby alleviating the burden imposed on the most upstream parts of the distribution network. As a consequence, the proposed method contributes to a power-efficient network, enhancing its loss performance and voltage stability.

关键词： electrical network minimization losses voltage deviation genetic algorithm firefly algorithm

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Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems

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ENERGY 2020年 210卷 118599-118599页

作者： Javed, Muhammad Shahzad Ma, Tao Jurasz, Jakub Ahmed, Salman Mikulik, Jerzy Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China MDH Univ Sch Business Soc & Engn Vasteras Sweden AGH Univ Sci & Technol Fac Management Krakow Poland

Hybrid renewable energy systems have been widely acknowledged as a clean, affordable and reliable mechanism to generate electricity and to accomplish global sustainable development goals. In this study, first, an operating strategy and an optimization problem are developed for a hybrid, off-grid, solar-wind system based on pumped hydro battery storage, and then a non-linear optimization problem is described for the considered system. To solve the optimization problem, four different optimization techniques are employed i.e. ant colony (ACO), firefly algorithm (FA), particle swarm optimization (PSO) and genetic algorithm (GA) and their performance is compared using statistical parameters like relative error, mean absolute error and root mean square error. Each optimization technique's working principle is discussed in detail and formulated considering the proposed optimization problem. The exploration and exploitation behavior of each algorithm is comprehensively analyzed explaining that ACO and FA have higher exploitation behavior, while GA and PSO have more exploration behavior, revealing that these behavior depend on the range of operator controlling parameters, type of optimization problem and formulation structure of the optimizers. The reference controlling parameters of each optimizer (which are operator dependent) are defined for the proposed optimization problem. The results reveal that FA performs better - i.e. with the least relative error (0.126) - while PSO outperforms best in terms of least objective function value (0.2435 $/kWh). The mean efficiency of each algorithm in terms of repeated executions (30 times) is ACO = 95.94%, FA = 96.20%, GA = 93.93%, PSO = 96.20%. The proposed study could help decision-makers to choose an optimization method to solve non-linear problems in the context of storage-based, off-grid systems under different scenarios. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Hybrid pumped-hydro-battery-storage Ant colony firefly algorithm Particle swarm optimization Genetic algorithm Statistical analysis

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Feasibility of indirect measurement of bearing capacity of driven piles based on a computational intelligence technique

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MEASUREMENT 2020年第0期156卷 107577-000页

作者： Sun, Guoquan Hasanipanah, Mahdi Amnieh, Hassan Bakhshandeh Foong, Loke Kok Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Tehran Coll Engn Sch Min Tehran 111554563 Iran Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Sinosteel Maanshan Gen Inst Min Res Co Ltd Maanshan 243000 Peoples R China

Parametric studies of driven piles response during dynamic and static load experiments, especially in the field, are generally too costly to measure. This study constructs and verifies a computational intelligence technique for predicting the bearing capacity of driven piles. The proposed technique is a hybridization of adaptive neuro-fuzzy inference system (ANFIS) and firefly algorithm (FA). It is worth mentioning that this is the first work that explores the ability of FA-ANFIS in the field of piles capacity prediction. In modeling, different FA-ANFIS and ANFIS models were constructed based on different combination of input parameters, and finally, the performances of all models were compared using statistical functions. The results demonstrated that the proposed FA-ANFIS model with coefficient of determination (R-2) of 0.997 was more effective than the ANFIS in predicting the pile capacity, and thereby the effectiveness of FA to optimize ANFIS was proved. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Driven piles ANFIS firefly algorithm Hybrid prediction model

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Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

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REVISTA DE METALURGIA 2023年第2期59卷

作者： Somani, Nalin Walia, Arminder Singh Gupta, Nitin Kumar Panda, Jyoti Prakash Das, Anshuman Das, Sudhansu Ranjan DIT Univ Dept Mech Engn Dehra Dun India Thapar Polytech Coll Patiala Punjab India VSSUT Burla Dept Prod Engn Burla Odisha India

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (Ip), Pulse on time (Ton) and Pulse off time (Toff) were the significant parameters which were affecting the tool wear rate (TWR) while the Ip, Ton and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in Ip were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses. leads to increase in the wear rate of the tool. The data obtained from experiments

关键词： Data driven modeling Electric discharge machining firefly algorithm Machine learning Surface roughness Tool wear

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Research on LFFA-BP neural network model in breakout prediction

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METALLURGICAL RESEARCH & TECHNOLOGY 2024年第3期121卷

作者： Zhang, Benguo Ma, Bangbang Sheng, Wanbao Zhang, Kaijun Wu, Di Zhang, Ruizhong Yancheng Inst Technol Sch Mech Engn Yancheng Peoples R China Hebei Iron & Steel Grp Mat Technol Res Inst Shijiazhuang Peoples R China

Advance forecasting of breakout in the continuous casting process could help to improve the capacity and quality of slabs. Neural network-based prediction methods are the main methods used for forecasting, but they have the disadvantages of being complicated and time-consuming. To compensate for these shortcomings, Levy Flight (LF) and firefly algorithm (FA) are introduced into a Back Propagation (BP) neural network to establish the LFFA-BP network model, which applied to the prediction of breakout. First, the model utilizes LF to change the step size of FA to prevent it from falling into local optimal solutions. Subsequently, the network optimal threshold is determined through the powerful search capability of the FA. Lastly, the network computation time is reduced through the superior convergence speed of FA. The models before and after improvement are used to classify and predict the temperature data collected at a production site. The results show that the identification accuracy of the LFFA-BP breakout prediction model is significantly higher than that of the traditional BP breakout prediction model, since it achieved a prediction accuracy of 99.23% and reporting rate of 100%. The improved model not only accelerates the running speed of the network model, but also ensures its global search capability and robustness, indicating that it has good application prospects.

关键词： firefly algorithm Levy flight BP neural network breakout prediction continuous casting

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Global Maximum Power Point Tracking of Partially Shaded PV System Using Advanced Optimization Techniques

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ENERGIES 2022年第11期15卷 4055页

作者： Akram, Nouman Khan, Laiq Agha, Shahrukh Hafeez, Kamran COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad 44000 Pakistan

In this work, a meta-heuristic optimization based method, known as the firefly algorithm (FA), to achieve the maximum power point (MPP) of a solar photo-voltaic (PV) system under partial shading conditions (PSC) is investigated. The firefly algorithm outperforms other techniques, such as the Perturb & Observe (P&O) method, proportional integral derivative (PID, and particle swarm optimization (PSO). These results show that the firefly algorithm (FA) tracks the MPP accurately compared with other above mentioned techniques. The PV system performance parameters i.e., convergence and tracking speed, is improved compared to conventional MPP tracking (MPPT) algorithms. It accurately tracks the various situations that outperform other methods. The proposed method significantly increased tracking efficiency and maximized the amount of energy recovered from PV arrays. Results show that FA exhibits high tracking efficiency (>99%) and less convergence time (<0.05 s) under PSCs with less power oscillations. All of these methods have been validated in Matlab simulation software.

关键词： maximum power point tracking firefly algorithm photovoltaic system

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