Records with Keyword: Artificial Intelligence
Showing records 1 to 25 of 197. [First] Page: 1 2 3 4 5 Last
Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review
Sergey Zhironkin, Ekaterina Taran
May 23, 2023 (v1)
Subject: Energy Policy
Keywords: Artificial Intelligence, Industry 4.0, Internet of Things, Surface Mining 4.0, technological shock, unmanned equipment
The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource con... [more]
Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks
Hyeon-Jun Kim, Soo-Whang Baek
April 28, 2023 (v1)
Keywords: Artificial Intelligence, internet of things, LSTM, neural network, RNN, wearable
This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coor... [more]
Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives
Dorian Skrobek, Jaroslaw Krzywanski, Marcin Sosnowski, Ghulam Moeen Uddin, Waqar Muhammad Ashraf, Karolina Grabowska, Anna Zylka, Anna Kulakowska, Wojciech Nowak
April 28, 2023 (v1)
Keywords: Artificial Intelligence, deep learning, energy processes and systems, Machine Learning, neural networks
In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940−2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artific... [more]
Understanding & Screening of DCW through Application of Data Analysis of Experiments and ML/AI
Tony Thomas, Pushpa Sharma, Dharmendra Kumar Gupta
April 28, 2023 (v1)
Subject: Environment
Keywords: Artificial Intelligence, experimentation, Machine Learning, oil recovery mechanism, sustainable development, waterflood
An oil recovery technique, different composition waterflooding (DCW), dependent on the varying injected water composition has been the subject of various research work in the past decades. Research work has been carried out at the lab, well and field scale whereby the introduction of different injection water composition vis-a-vis the connate water is seen to bring about improvements in the oil recovery (improvements in both macroscopic and microscopic recoveries) based on the chemical reactions, while being sustainable from ease of implementation and reduced carbon footprint points of view. Although extensive research has been conducted, the main chemical mechanisms behind the oil recovery are not yet concluded upon. This research work performs a data analysis of the various experiments, identifies gaps in existing experimentation and proposes a comprehensive experimentation measurement reporting at the system, rock, brine and oil levels that leads to enhanced understanding of the und... [more]
Artificial Intelligence Methods in Hydraulic System Design
Grzegorz Filo
April 28, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, evolutionary algorithms, fuzzy logic, hydraulic system design
Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions
Iftikhar Ahmad, Adil Sana, Manabu Kano, Izzat Iqbal Cheema, Brenno C. Menezes, Junaid Shahzad, Zahid Ullah, Muzammil Khan, Asad Habib
April 24, 2023 (v1)
Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models,... [more]
The Review of Carbon Capture-Storage Technologies and Developing Fuel Cells for Enhancing Utilization
Nehil Shreyash, Muskan Sonker, Sushant Bajpai, Saurabh Kr Tiwary, Mohd Ashhar Khan, Subham Raj, Tushar Sharma, Susham Biswas
April 24, 2023 (v1)
Keywords: Artificial Intelligence, Carbon Capture, CO2 combustion, electrochemical conversion, fuel cell, storage and utilization
The amount of CO2 released in the atmosphere has been at a continuous surge in the last decade, and in order to protect the environment from global warming, it is necessary to employ techniques like carbon capture. Developing technologies like Carbon Capture Utilization and Storage aims at mitigating the CO2 content from the air we breathe and has garnered immense research attention. In this review, the authors have aimed to discuss the various technologies that are being used to capture the CO2 from the atmosphere, store it and further utilize it. For utilization, researchers have developed alternatives to make profits from CO2 by converting it into an asset. The development of newer fuel cells that consume CO2 in exchange for electrical power to drive the industries and produce valuable hydrocarbons in the form of fuel has paved the path for more research in the field of carbon utilization. The primary focus on the article is to inspect the environmental and economic feasibility of n... [more]
AI and Data Democratisation for Intelligent Energy Management
Vangelis Marinakis, Themistoklis Koutsellis, Alexandros Nikas, Haris Doukas
April 24, 2023 (v1)
Keywords: Artificial Intelligence, data democratisation, data sharing, decarbonisation, decision support, energy data spaces, energy management, interoperability
Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation fr... [more]
An Artificial Intelligence Empowered Cyber Physical Ecosystem for Energy Efficiency and Occupation Health and Safety
Petros Koutroumpinas, Yu Zhang, Steve Wallis, Elizabeth Chang
April 21, 2023 (v1)
Subject: Environment
Keywords: Artificial Intelligence, COVID-19, cyber-physical system, ecosystem, Energy Efficiency, OHS, remote industries, smart meter
Reducing energy waste is one of the primary concerns facing Remote Industrial Plants (RIP) and, in particular, the accommodations and operational plants located in remote areas. With the COVID-19 pandemic continuing to attack the health of workforce, managing the balance between energy efficiency and Occupation Health and Safety (OHS) in the workplace becomes another great challenge for the RIP. Maintaining this balance is difficult mainly because a full awareness of the OHS will generally consume more energy while reducing the energy cost may lead to a less effective OHS, and the existing literature has not seen a system that is designed for the RIPs to conserve energy usage and improve workforce OHS simultaneously. To bridge this gap, in this paper, we propose an AI Empowered Cyber Physical Ecosystem (AECPE) solution for the RIPs, which integrates Cyber-Physical Systems (CPS), artificial intelligence, and mobile networks. The preliminary results of lab experiments and field tests pro... [more]
Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems
Hafiz M. Asfahan, Uzair Sajjad, Muhammad Sultan, Imtiyaz Hussain, Khalid Hamid, Mubasher Ali, Chi-Chuan Wang, Redmond R. Shamshiri, Muhammad Usman Khan
April 21, 2023 (v1)
Keywords: Artificial Intelligence, direct evaporative cooling, evaporative cooling, indirect evaporative cooling, Maisotsenko evaporative cooling
The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the... [more]
Digital Transformation of Energy Companies: A Colombian Case Study
Sandra Giraldo, David la Rotta, César Nieto-Londoño, Rafael E. Vásquez, Ana Escudero-Atehortúa
April 19, 2023 (v1)
Subject: Energy Policy
Keywords: Artificial Intelligence, digital transformation, energy commercialization, energy trading, energy transition, hydropower projects, Industry 4.0, Renewable and Sustainable Energy, risk management
The United Nations established 17 Sustainable Development Goals (SDGs), and the fulfillment of the 7th, defined as “Ensure access to affordable, reliable, sustainable, and modern energy for all”, requires energy industry transitions and digital transformations, which implies that diverse stakeholders need to move fast to allow the growth of more flexible power systems. This paper contains the case report that addresses the commercial digital transformation process developed at AES Colombia, through the implementation of a modern platform based on specialized applications that use Industry 4.0 tools. The Chivor hydropower project, a 1000-MW powerplant that covers 6% of Colombia’s demand, which is owned by AES Colombia and constitutes its primary asset, is first described. Then, a description of Colombia’s complex market (energy matrix, trading and dispatch mechanisms, and future projects) is presented. Then, the methodology followed for the digital transformation process using modern to... [more]
Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence
Sofia Agostinelli, Fabrizio Cumo, Giambattista Guidi, Claudio Tomazzoli
April 19, 2023 (v1)
Keywords: Artificial Intelligence, digital construction, digital twin, edge computing, Energy Efficiency, energy management, nZEB
The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of th... [more]
Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline
Youngjin Seo, Byoungjun Kim, Joonwhoan Lee, Youngsoo Lee
April 19, 2023 (v1)
Subject: Optimization
Keywords: Artificial Intelligence, diagnostic model, gas hydrate, greedy layer-wise, stacked auto-encoder
For the stable supply of oil and gas resources, industry is pushing for various attempts and technology development to produce not only existing land fields but also deep-sea, where production is difficult. The development of flow assurance technology is necessary because hydrate is aggregated in the pipeline and prevent stable production. This study established a system that enables hydrate diagnosis in the gas pipeline from a flow assurance perspective. Learning data were generated using an OLGA simulator, and temperature, pressure, and hydrate volume at each time step were generated. Stacked auto-encoder (SAE) was used as the AI model after analyzing training loss. Hyper-parameter matching and structure optimization were carried out using the greedy layer-wise technique. Through time-series forecast, we determined that AI diagnostic model enables depiction of the growth of hydrate volume. In addition, the average R-square for the maximum hydrate volume was 97%, and that for the form... [more]
Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine
Ethelbert Ezemobi, Andrea Tonoli, Mario Silvagni
April 19, 2023 (v1)
Keywords: Artificial Intelligence, automotive, Batteries, electric vehicles, energy reliability, hybrid vehicles, improved generalization, online application, parallel layer extreme learning machine, state of health
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed... [more]
Examination of Multivalent Diagnoses Developed by a Diagnostic Program with an Artificial Neural Network for Devices in the Electric Hybrid Power Supply System “House on Water”
Stanisław Duer, Konrad Zajkowski, Marta Harničárová, Henryk Charun, Dariusz Bernatowicz
April 19, 2023 (v1)
Keywords: Artificial Intelligence, diagnostic reasoning, hybrid power system, multivalent logic, technical diagnostics
This article presents the problem of diagnostic examination by the (DIAG) diagnostic system of devices of the House on Water (HoW) hybrid electric power system in the multi-valued (2, 3, and 4) state assessment. Forming the basis for the functioning of the (DIAG) diagnostic system is the measurement knowledge base of the object tested. For this purpose, the issues of building a diagnostic knowledge base for a hybrid power system for HoW are presented. The basis for obtaining diagnostic information for the measurement knowledge base is a functional and diagnostic analysis of the hybrid power system tested. The result of this analysis is a functional and diagnostic model of the research object. At the next stage of the work, on the basis of the model created, the sets of basic elements and the sets of measurement signals were determined together with the reference signals assigned. State classification in the (DIAG) system is based on an analysis of the value of the divergence metrics of... [more]
Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks
Elias Kaufhold, Simon Grandl, Jan Meyer, Peter Schegner
April 19, 2023 (v1)
Keywords: Artificial Intelligence, converter, Modelling, photovoltaics, power electronics, power quality
This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstr... [more]
Liquified Petroleum Gas-Fuelled Vehicle CO2 Emission Modelling Based on Portable Emission Measurement System, On-Board Diagnostics Data, and Gradient-Boosting Machine Learning
Maksymilian Mądziel
April 18, 2023 (v1)
Keywords: Artificial Intelligence, Carbon Dioxide, emission modelling, LPG, Machine Learning, portable emission measurement system, vehicle emission
One method to reduce CO2 emissions from vehicle exhaust is the use of liquified petroleum gas (LPG) fuel. The global use of this fuel is high in European countries such as Poland, Romania, and Italy. There are a small number of computational models for the purpose of estimating the emissions of LPG vehicles. This work is one of the first to present a methodology for developing microscale CO2 emission models for LPG vehicles. The developed model is based on data from road tests using the portable emission measurement system (PEMS) and on-board diagnostic (OBDII) interface. This model was created from a previous exploratory data analysis while using gradient-boosting machine learning methods. Vehicle velocity and engine RPM were chosen as the explanatory variables for CO2 prediction. The validation of the model indicates its good precision, while its use is possible for the analysis of continuous CO2 emissions and the creation of emission maps for environmental analyses in urban areas. T... [more]
Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control
Matteo Acquarone, Claudio Maino, Daniela Misul, Ezio Spessa, Antonio Mastropietro, Luca Sorrentino, Enrico Busto
April 18, 2023 (v1)
Keywords: Artificial Intelligence, fuel consumption, hybrid electric vehicles, real-time control, reinforcement learning
The real-time control optimization of electrified vehicles is one of the most demanding tasks to be faced in the innovation progress of low-emissions mobility. Intelligent energy management systems represent interesting solutions to solve complex control problems, such as the maximization of the fuel economy of hybrid electric vehicles. In the recent years, reinforcement-learning-based controllers have been shown to outperform well-established real-time strategies for specific applications. Nevertheless, the effects produced by variation in the reward function have not been thoroughly analyzed and the potential of the adoption of a given RL agent under different testing conditions is still to be assessed. In the present paper, the performance of different agents, i.e., Q-learning, deep Q-Network and double deep Q-Network, are investigated considering a full hybrid electric vehicle throughout multiple driving missions and introducing two distinct reward functions. The first function aim... [more]
A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities
Harleen Kaur Sandhu, Saran Srikanth Bodda, Abhinav Gupta
April 18, 2023 (v1)
Keywords: advanced reactors, Artificial Intelligence, concrete, condition assessment, damage detection, data management, deep learning, digital twin, nuclear piping, signal processing
The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali−silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of exist... [more]
Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation
Arim Jin, Dahan Lee, Jong-Bae Park, Jae Hyung Roh
April 17, 2023 (v1)
Keywords: Artificial Intelligence, data preprocessing, decomposition, electricity market, feature selection, price forecast
This paper aims to improve the forecasting of electricity market prices by incorporating the characteristics of electricity market prices that are discretely affected by the fuel cost per unit, the unit generation cost of the large-scale generators, and the demand. In this paper, two new techniques are introduced. The first technique applies feature generation to the label and forecasts the transformed new variables, which are then post-processed by inverse transformation, considering the characteristic of the fuel types of marginal generators or prices through two variables: fuel cost per unit by the representative fuel type and argument of the maximum of Probability Density Function (PDF) calculated by Kernel Density Estimation (KDE) from the previous price. The second technique applies decomposition to the demand, followed by a feature selection process to apply the major decomposed feature. It is verified using gain or SHapley Additive exPlanations (SHAP) value in the feature selec... [more]
Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland
Justyna Łapińska, Iwona Escher, Joanna Górka, Agata Sudolska, Paweł Brzustewicz
April 14, 2023 (v1)
Keywords: Artificial Intelligence, chemical industry, employees, energy industry, trust
The use of artificial intelligence (AI) in companies is advancing rapidly. Consequently, multidisciplinary research on AI in business has developed dramatically during the last decade, moving from the focus on technological objectives towards an interest in human users’ perspective. In this article, we investigate the notion of employees’ trust in AI at the workplace (in the company), following a human-centered approach that considers AI integration in business from the employees’ perspective, taking into account the elements that facilitate human trust in AI. While employees’ trust in AI at the workplace seems critical, so far, few studies have systematically investigated its determinants. Therefore, this study is an attempt to fill the existing research gap. The research objective of the article is to examine links between employees’ trust in AI in the company and three other latent variables (general trust in technology, intra-organizational trust, and individual competence trust).... [more]
Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings
Hossein Moayedi, Amir Mosavi
April 14, 2023 (v1)
Keywords: Artificial Intelligence, Big Data, building energy, cooling load, deep learning, energy-efficiency, HVAC, Machine Learning, nature-inspired metaheuristic, smart buildings, smart city, zero energy
Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS−ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying... [more]
Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings
Hossein Moayedi, Amir Mosavi
April 14, 2023 (v1)
Keywords: air conditioning, Artificial Intelligence, Big Data, consumption prediction, deep learning, Energy Efficiency, heating loads, heating ventilation, Machine Learning, metaheuristic, operational research
A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation =... [more]
An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework
Hossein Moayedi, Amir Mosavi
April 14, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, Big Data, deep learning, electrical power modeling, Machine Learning, metaheuristic, photovoltaic, solar energy, solar irradiance, solar power
Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for th... [more]
A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building
Roozbeh Sadeghian Broujeny, Kurosh Madani, Abdennasser Chebira, Veronique Amarger, Laurent Hurtard
April 13, 2023 (v1)
Keywords: ambient intelligence, Artificial Intelligence, Energy Efficiency, fuzzy inference system, heating controller, smart building
Most already advanced developed heating control systems remain either in a prototype state (because of their relatively complex implementation requirements) or require very specific technologies not implementable in most existing buildings. On the other hand, the above-mentioned analysis has also pointed out that most smart building energy management systems deploy quite very basic heating control strategies limited to quite simplistic predesigned use-case scenarios. In the present paper, we propose a heating control strategy taking advantage of the overall identification of the living space by taking advantage of the consideration of the living space users’ presence as additional thermal sources. To handle this, an adaptive controller for the operation of heating transmitters on the basis of soft computing techniques by taking into account the diverse range of occupants in the heating chain is introduced. The strategy of the controller is constructed on a basis of the modeling heating... [more]
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