Search Results - (( java adaptation optimization algorithm ) OR ( demand prediction using algorithm ))

Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia by Zubaidi S.L., Kumar P., Al-Bugharbee H., Ahmed A.N., Ridha H.M., Mo K.H., El-Shafie A.

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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…The combined influence of the genetic algorithm and correlation analysis are used in this technique. …”
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Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction by Musa, Wahab, Ku Mahamud, Ku Ruhana, Salim, Sardi, Sediyono, Agung

“…This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. …”
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A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Chun T.S., Malek M.A., Ismail A.R.

Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm by Ting S.C., Ismail A.R., Malek M.A.

“…This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. …”

An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…Particularly, the proposed optimized Bagged Trees are the most effective algorithm for energy demand prediction applications, and the proposed optimized Medium Trees are the most efficient algorithm for real-time systems. …”
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Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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Prediction of biochemical oxygen demand in Mexican surface waters using machine learning / Maximiliano Guzmán-Fernández ... [et al.] by Maximiliano, Guzmán-Fernández, Misael, Zambrano-de la Torre, Claudia, Sifuentes-Gallardo, Oscar, Cruz-Dominguez, Carlos, Bautista-Capetillo, Juan, Badillo-de Loera, Efrén, González Ramírez, Héctor, Durán-Muñoz

“…Pearson’s correlation and Forward Selection techniques were applied to identify the parameters with the most important contribution to prediction of biochemical oxygen demand. Two groups were formed and used as input to four machine learning algorithms. …”
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Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

“…The predictive performance of these algorithms are evaluated using Australian electricity demand data. …”
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Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

“…The predictive performance of these algorithms are evaluated using Australian electricity demand data. …”
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Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

“…The predictive performance of these algorithms are evaluated using Australian electricity demand data. …”
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Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer by Salleh N.S.M., Suliman A., J�rgensen B.N.

One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

“…By using 'seen' and 'unseen' of electrical energy demand data were used to test the performance of the proposed algorithm. …”
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Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm by Sie Chun, Ting, Ismail , Amelia Ritahani, A. Malek, Marlinda

“…Keywords: Artificial Immune System; Chemical Oxygen Demand; Prediction; Septic Sludge Treatment Plant; Total Suspended Solids…”
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Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction by Tan, James Yiaw Beng

“…The evaluation results the proposed KGA model using several time series, namely the sunspot data, the Mackey-Glass time series, and electrical load forecasting using data from several econometric factors, as well as historical electricity demand data, show that the proposed KGA model is eflective in finding the optimal number ofneurons for the hidden layer of a BP network that is used to perform time series prediction.…”
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A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Ting, Sie Chun, Abdul Malik, Marlinda, Ismail, Amelia Ritahani

“…In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. …”
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Data mining techniques for transformer failure prediction model: A systematic literature review by Ravi N.N., Mohd Drus S., Krishnan P.S.

Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration by Abdullah S.S., Malek M.A., Abdullah N.S., Mustapha A.

Communication and computational cost on parallel algorithm of PDE elliptic type by Alias, Norma

“…The parallel algorithms of 2-dimensional Partial Differential Equation (PDE) elliptic type for the prediction will be executed using distributed memory of heterogeneous cluster platform on LINUX-based environment. …”
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