Combined heat and power economic dispatch (CHPED) problem is one of the most widely handled, optimization problem by researchers in modern power systems. CHPED problem is a complicated, non-continuous, and nonconvex o...
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Combined heat and power economic dispatch (CHPED) problem is one of the most widely handled, optimization problem by researchers in modern power systems. CHPED problem is a complicated, non-continuous, and nonconvex optimization problem due to the constraints. Moreover, considering the valve-point loading effect (VPLE), transmission losses (TLs), and prohibited operating zones (POZs) of power-only units as constraints, the complexity of CHPED problem increases. Therefore, a powerful optimizationalgorithm needs to be introduced to find global solution that meets all constraints. In this paper, a novel adaptive fitness-distance balance based artificialrabbitsoptimization (AFDB-ARO) is developed to solve CHPED problems. AFDB-based guiding mechanism was implemented to enhance the exploration capability of ARO and to strengthen exploitation-exploration balance. A comprehensive experimental study was realized to prove the performance of the proposed algorithm on the CHPED and benchmark problems. In experimental study between AFDB-ARO variants and ARO on 40 benchmark problems, according to Wilcoxon analysis results, all AFDB-ARO variants outperformed the base ARO, and the best AFDB-ARO variant won victory in 20 of 40 problem and achieved similar results in other 20 problem. In other experimental study, AFDB-ARO algorithm was implemented on the CHPED systems with 4-, 5-, 7-, 24-, 48-, 96-, and 192-units, and fifteen case studies were considered using these systems, VPLE, TLs, and POZs. One of the important points of this study was that POZs were considered for the first time in 96-and 192 -units system. The results show that AFDB-ARO achieved the best optimal solution in ten of fifteen cases, was same in one case, and obtained almost same results in four cases compared to the literature. Moreover, the stability of the AFDB-ARO and base ARO algorithms in solving the CHPED problem were tested by performing stability analysis. While the mean success rate, mean iteration num
作者:
Gulmez, BurakLeiden Univ
Leiden Inst Adv Comp Sci Leiden Netherlands Mine Apt
Altay Mah Sehit A Taner Ekici Sk 6 TR-06820 Ankara Turkiye
The stock market is a financial market where shares of publicly listed corporations are purchased and sold. It is an indicator of a country's economic health, reflecting the performance of companies and the overal...
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The stock market is a financial market where shares of publicly listed corporations are purchased and sold. It is an indicator of a country's economic health, reflecting the performance of companies and the overall business environment. The prices of stocks are determined by supply and demand. Investing in the stock market can be risky, but it can offer the potential for significant returns over the long term. artificial intelligence, including the stock market, has become increasingly prevalent in the financial sector. Long Short-Term Memory (LSTM) is a type of artificial neural network that is often used in time series analysis. It can effectively predict stock market prices by handling data with multiple input and output timesteps. Metaheuristic algorithms, such as artificial rabbits optimization algorithm (ARO), can be used to optimize the hyperparameters of an LSTM model and improve the accuracy of stock market predictions. In this paper, an optimized deep LSTM network with the ARO model (LSTM-ARO) is created to predict stock prices. DJIA index stocks are used as the dataset. LSTM-ARO is compared with one artificial neural network (ANN) model, three different LSTM models, and LSTM optimized by Genetic algorithm (GA) model. All the models are tested on MSE, MAE, MAPE, and R2 evaluation criteria. The results show that LSTM-ARO overcomes the other models.
It is becoming more important to use electric networks efficiently as power grids expand and modernize. Reducing existing challenges, such as excessive power losses, poor voltage profiles, voltage instability, unrelia...
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ISBN:
(纸本)9798350300338
It is becoming more important to use electric networks efficiently as power grids expand and modernize. Reducing existing challenges, such as excessive power losses, poor voltage profiles, voltage instability, unreliable operation, etc., is essential due to the high cost of constructing and expanding power grids. Today, minimizing losses in network distribution is crucial. On the other hand, distribution networks are using distributed generation sources (DGs) much more commonly. o prevent these problems, Distribution Synchronous Static Compensator (D-STACTOM) can be used in electric distribution networks as a shunt compensator device. Economic feasibility, needed quality, dependability, and availability should all be considered while deciding on the best location and size for D-STACOM. Therefore, by picking the appropriate place and size, these resources can play a crucial part in lowering the power losses of distribution networks. D-FACTS tools like D-STATCOM in distribution networks and DGs can significantly contribute to reducing losses and compensating reactive power. The optimal sizing & placement of DGs and D-STATCOM in the radial distribution network is covered in this article. The proposed method's desired outcomes include lowering active power losses, enhancing voltage stability and profile, and minimizing costs. The artificialrabbitsoptimization (ARO) algorithm has resolved this optimization issue. The IEEE 33 bus standard system tests the suggested method for analysis. The results are compared with two algorithms, Harris Hawks optimization (HHO) and Emperor Penguins Colony (EPS), to investigate the capability of the algorithm proposed for optimal sizing and placement problems. According to the simulation results, the ideal placement and size for DGs and D-STATCOM can significantly lower network losses and enhance the voltage profile.
Numerous sectors are significantly impacted by the quick advancement of image and video processing technologies. Investors can kind knowledgeable savings choices based on the examination and projection of financial ba...
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Numerous sectors are significantly impacted by the quick advancement of image and video processing technologies. Investors can kind knowledgeable savings choices based on the examination and projection of financial bazaar income, and the government can create accurate policies for various forms of economic control. This study uses an artificial rabbits optimization algorithm in image processing technology to examine and forecast the returns on financial markets and multiple indexes using a deep-learning LSTM network. This research uses the time series technique to record the regional correlation properties of financial market data. Convolution pooling in LSTM is then used to gather significant details concealed in the time sequence information, generate the data's tendency bend, and incorporate the structures using technology for image processing to ultimately arrive at the forecast of the economic sector's moment series earnings index. A popular artificial neural network used in time series examination is the long short-term memory (LSTM) network. It can accurately forecast financial marketplace values by processing information with numerous input and output timesteps. The correctness of financial market predictions can be increased by optimizing the hyperparameters of an LSTM model using metaheuristic procedures like the artificial rabbits optimization algorithm (ARO). This research presents the development of an enhanced deep LSTM network with the ARO method (LSTM-ARO) for stock price prediction. According to the findings, the research's deep learning system for financial market series prediction is efficient and precise. Data analysis and image processing technologies offer practical approaches and significantly advance finance studies.
An online thickness prediction algorithm for strip steel based on machine learning is proposed to address issues of strong coupling and low accuracy in the thickness mathematical model. Firstly, the rolling data is us...
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ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387780;9798350387797
An online thickness prediction algorithm for strip steel based on machine learning is proposed to address issues of strong coupling and low accuracy in the thickness mathematical model. Firstly, the rolling data is used to establish an online thickness prediction model by optimizing the eXtreme Gradient Boosting (XGBoost) algorithm parameters based on the artificialrabbitsoptimization (ARO) algorithm. Then, a self-learning system is deployed to optimize the results. Finally, the predicted outcomes will be compared with the actual thickness to verify the accuracy of the thickness prediction model. The experimental results show that the online thickness prediction algorithm can quickly and accurately predict the strip thickness. When the ARO-XGBoost model was used to predict the 3 mm, 4 mm, and 5.65 mm strips, Root Mean Square Error (RMSE) can be controlled within 11.5 mu m, 12 mu m, and 16.6 mu m. The results can improve the accuracy of the thickness mathematical model for hot strip rolling and enhance the level of the thickness control system.
This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensu...
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Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parame...
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