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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

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Short term forecasting based on hybrid least squares support vector machines by Zuriani, Mustaffa, M. H., Sulaiman, Ernawan, Ferda, Noorhuzaimi, Mohd Noor

“…Later, the performance of each identified hybrid algorithm is analyzed and discussed. From the simulations, it is demonstrated that all the identified algorithms are able to produce better forecasting result by using normalized time series data.…”
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Long term energy demand forecasting based on hybrid, optimization: Comparative study by Musa, Wahab, Ku-Mahamud, Ku Ruhana, Yasin, Azman

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Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

“…While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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Virtual power plant and microgrids controller for energy management based on optimization techniques by Abdolrasol M.G.M., Mohamed A., Hannan M.A.

Hybrid ANN and Artificial Cooperative Search Algorithm to Forecast Short-Term Electricity Price in De-Regulated Electricity Market by Pourdaryaei, Alireza, Mokhlis, Hazlie, Illias, Hazlee Azil, Kaboli, S. Hr. Aghay, Ahmad, Shameem, Ang, Swee Peng

“…Therefore, this research proposes a hybrid method for electricity price forecasting via artificial neural network (ANN) and artificial cooperative search algorithm (ACS). …”
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Multi-scene design analysis of integrated energy system based on feature extraction algorithm by Huang, Sihua, Mohd Ali, Noor Azizi, Shaari, Nazlina, Mat Noor, Mohd Sallehuddin

“…The errors between the predicted results of various energy loads and the actual load records in the study area are 1.105%, 1.876%, 3.102% and 2.834%, respectively. The load forecasting method based on feature clustering proposed in this paper can effectively extract the influence of different environmental factors on the load forecasting results, and get more accurate load forecasting results.…”
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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.

“…Keywords - artificial neural network; mean absolute percentage error; genetic algorithm; simulated annealing; correlation analysisAbstract — Electrical energy demand forecasting plays a pivotal role as a decision support tool in the modern power industry. …”
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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.

Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…Finally, future estimations of EEC in ASEAN-5 countries are projected up to 2030 by applying the rolling-based forecasting procedure on mathematical models derived from optimized GEP. …”
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Gasoline price forecasting: An application of LSSVM with improved ABC by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

“…Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IABC serves as an optimizer for LSSVM.Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE).The conducted simulation results show that, the proposed IABCLSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network.…”
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SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

“…The SURE-Autometrics is also validated using two sets of real data by comparing the forecast error measures with five model selection algorithms and three non-algorithm procedures. …”
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Development of a multi criteria decision support system using convolutional neural network and jaya algorithm for water resources management / Chong Kai Lun by Chong , Kai Lun

“…The proposed technique was then tested on a dataset obtained from the same hydrological stations used when the forecasting modeling. According to the simulated results, the proposed model can provide a statistical distribution of the forecasted quantity. …”
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Optimization of operational policies for the Minab Reservoir, Southern Iran by Gholampoor, Mohammad

“…The Soil and Water Assessment Tools (SWAT) model and the Focused Time Delay Recurrent Neural Network (F.T.D.N.N) method were used to simulate and forecast future inflow to the reservoir by considering stream flow factors and their constraints. …”
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Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin by Ahmad Kamaruddin, Saadi

“…Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the ordinary least squares (LS) estimator in terms of mean squared error (MSE). …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab by Saad Mawlood , Saab

“…A well-designed and reliable forecasting model is key to the successful reservoir simulation so as to maximize the use of water resources. …”
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Leveraging artificial intelligence in modern supply chains by Ng, Qiao Ying

“…A synthetic dataset was generated to simulate urban traffic flows, and a Long Short-Term Memory (LSTM) model was trained to forecast short-term congestion patterns. …”
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Application of Evolutionary Algorithm for Assisted History Matching by Zahari, Muhammad Izzat

“…Evolutionary method helps to find the global minima directly without the presence of local minima. Besides, algorithm based method has been widely used to forecast future result in various field for example art, biology, marketing including engineering. …”
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Spatial Simulation for Flood Disaster Forecasting: A Geospatial Technology-based Group Analytic Hierarchy Process Approach by Dano Lawal, Umar, Matori, Abdul Nasir, Wan Yusof, Khamaruzaman, Mustafa Hashim , Ahmad, Aminu, Mansir, Sabri, Soheil, Balogun, Abdul-Lateef, Ahmed Chandio, Imtiaz, Radin Mohd Mokhtar, Munirah

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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

“…In this study, the performance of these three algorithms in obtaining the optimal blade design based on the �436�45D are investigated and compared. …”
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