Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks

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Energy Planning Model Design for Forecasting the Final Energy Consumption Using Artificial Neural Networks

Author : Haidy Eissa
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Energy Planning Model Design for Forecasting the Final Energy Consumption Using Artificial Neural Networks by : Haidy Eissa

Download or read book Energy Planning Model Design for Forecasting the Final Energy Consumption Using Artificial Neural Networks written by Haidy Eissa and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Energy Trilemma" has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation's capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the years, there have been many attempts to develop energy system models, which have varying degrees of success. Artificial Neural Networks (ANN) is one of the modeling techniques that shows great performance in modeling the energy consumption side, which has the time-series characteristics of complexity and nonlinearity. Static feedforward neural networks are extensively used in literature due to their simplicity. In this thesis, we propose two artificial neural network topologies: feedforward and Nonlinear Autoregressive with Exogenous Inputs (NARX) neural networks, where four separate ANN models, are formulated to study and forecast the annual final energy consumption for four different sectors in the United Kingdom till 2035: transport, domestic, services, industrial. The outputs of all models are finally summed up to yield UK's total final energy consumption. Furthermore, in this thesis, we use the Bayesian optimization algorithm to search for the optimal network hyperparameters. Moreover, instead of arbitrarily selecting input parameters in a qualitative manner, a sequential backward selection technique is used to exclude any uninformative input variables that have no predictive power, and eventually select the optimal set of input parameters. The network performance is measured with regards to various accuracy metrics such as Root Mean Square Error (RMSE), and Mean Average Percentage Error (MAPE). The resulting forecasts are eventually compared to the final energy consumption outlook from UK's governmental Department for Business, Energy & Industrial Strategy (BEIS). The comparisons show that the NARX model offers superior results compared to the feedforward model in terms of the accuracy metrics as well as the long-term forecasts. Moreover, the developed NARX model succeeded in having a better performance than other models developed in the literature.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Python-based Deep-Learning Methods for Energy Consumption Forecasting

Author : Josep Roman Cardell
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Python-based Deep-Learning Methods for Energy Consumption Forecasting by : Josep Roman Cardell

Download or read book Python-based Deep-Learning Methods for Energy Consumption Forecasting written by Josep Roman Cardell and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In a society where we do nothing but increase the use of electricity in our daily life, en-ergy consumption and the corresponding management is a major issue. The predictionof electric energy demand is a key component, for the power system operators, in themanagement of the electrical grid. The importance of forecasting a particular house-hold daily energy consumption does concern the end-user too, by reason of the designand sizing of a suitable renewable energy system and energy storage.The aim of this thesis is to develop and train a computing system capable of predict-ing, with best accuracy as possible, electricity consumption at household-level. Thispaper presents a Short Term Load Forecasting (STLF) with Artificial Neural Networks(ANN), which lead to accurate results in spite of the dwelling consumption unpre-dictability. The recorded data, containing the daily track of electricity consumption overa particular household from 2015 to 2018, was analysed. Subsequently, a study over theANN architecture and training algorithms was carried out in order to define a robustmodel. Furthermore, several experiments were conducted with different models, con-taining distinct inputs, aiming to compare the relevance of a diversity of parametersfor the network's training. Finally, the forecasting of the optimal models, created withthe insights collected over the whole research, was performed and compared in severalspecially selected time periods.The results showed how with the appropriate inputs and selection of hyperparame-ters, a shallow ANN can provide certain accuracy on the forecasting of electric energydemand. As well as a methodology to develop and train an artificial neural network.

Estimating Construction Costs

Author : Robert Leroy Peurifoy
Publisher : McGraw-Hill
ISBN 13 : 9780071239455
Total Pages : 560 pages
Book Rating : 4.2/5 (394 download)

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Book Synopsis Estimating Construction Costs by : Robert Leroy Peurifoy

Download or read book Estimating Construction Costs written by Robert Leroy Peurifoy and published by McGraw-Hill. This book was released on 2001-12-01 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Peurifoy was a giant in the field of construction engineering and authored several books during his lifetime. This book last published in 1989 and will capitalize on the well-known name of the author. In this edition, computer calculations of costs and of modeling have been added as well as updated statistics, computer related examples and new problems. Civil, Environmental, and Construction Management Engineering Majors and Professionals will benefit from having this title on their shelf.This edition retains the conceptual strengths of the Peurifoy approach and organization from the previous edition but the new problems and computer-based examples and new up-to-date construction data make it the only choice in academia or industry.

From Natural to Artificial Neural Computation

Author : Jose Mira
Publisher : Springer Science & Business Media
ISBN 13 : 9783540594970
Total Pages : 1182 pages
Book Rating : 4.5/5 (949 download)

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Book Synopsis From Natural to Artificial Neural Computation by : Jose Mira

Download or read book From Natural to Artificial Neural Computation written by Jose Mira and published by Springer Science & Business Media. This book was released on 1995-05-24 with total page 1182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the proceedings of the International Workshop on Artificial Neural Networks, IWANN '95, held in Torremolinos near Malaga, Spain in June 1995. The book contains 143 revised papers selected from a wealth of submissions and five invited contributions; it covers all current aspects of neural computation and presents the state of the art of ANN research and applications. The papers are organized in sections on neuroscience, computational models of neurons and neural nets, organization principles, learning, cognitive science and AI, neurosimulators, implementation, neural networks for perception, and neural networks for communication and control.

Data Mining and Machine Learning in Building Energy Analysis

Author : Frédéric Magoules
Publisher : John Wiley & Sons
ISBN 13 : 1118577590
Total Pages : 186 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Data Mining and Machine Learning in Building Energy Analysis by : Frédéric Magoules

Download or read book Data Mining and Machine Learning in Building Energy Analysis written by Frédéric Magoules and published by John Wiley & Sons. This book was released on 2016-01-05 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

Graph-Based Model for Building Energy Prediction

Author : Atefeh Shamloo
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)

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Book Synopsis Graph-Based Model for Building Energy Prediction by : Atefeh Shamloo

Download or read book Graph-Based Model for Building Energy Prediction written by Atefeh Shamloo and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate and timely forecasting of building energy use is critical in mitigating the impacts of climate change. Buildings consume a significant amount of energy worldwide, and the ability to accurately predict their energy usage can inform energy-saving strategies, reduce energy waste, and promote sustainable energy practices. Therefore, timely and precise building energy prediction is essential for achieving global energy conservation goals and mitigating the effects of climate change. Many factors contribute to the energy efficiency of buildings such as space layout design, weather and location of the building. The arrangement of space plays a crucial role in the development of building design and can exert a considerable impact on the energy efficiency of the environment. However, the existing data-driven methods do not effectively take the space relations into account in order to predict the energy consumption of buildings. On the other hand, physic-based models can consider spatial relations by including multiple zones, but it can be computationally intensive and time-consuming. This study aims to investigate the impact of space relationships in a layout on building energy usage and aims to develop a model that can effectively integrate space information and their relations into building prediction methods without significantly increasing computational costs. Thus, the study seeks to address the limitations of existing data-driven building energy prediction models, which often neglect the complex spatial relationships in the building layout or rely on computationally intensive physics-based models. Specifically, this study proposes to use graph neural network for building energy prediction by utilizing the graph structure to abstract building space relations and integrate the space relations in model prediction. The Spatial-temporal Graph Neural Networks, which are an extension of Graph Neural Networks, have been developed to consider the temporal factors for hourly building energy use prediction. This study selects an office building layout as a baseline model to simulate the energy consumption of the building. To investigate the impact of layout variations on energy consumption, we systematically altered the arrangement of spaces within an office building. In this layout we have different functions with different setpoint temperatures. The simulations revealed that altering the spatial arrangement had a significant impact on energy consumption. The maximum annual heating difference observed among different layouts reached around 12%, highlighting the potential for substantial energy savings through strategic spatial planning. Similarly, the cooling energy consumption varied about 8% across the various layouts. Building upon these insights, this research develops a spatial temporal graph neural network model to construct a time-series machine learning model for the building, enabling to forecast the energy consumption. In order to assess the effectiveness of the proposed Spatial-Temporal Graph Neural Network model, a model comparison was conducted, considering alternative data-driven and regression-based approaches commonly employed in building energy prediction. Specifically, the ST-GNN was compared against Extreme Gradient Boosting (XGBoost) and Linear Regression (LR) models, which are widely used for their simplicity and interpretability. These models were chosen to represent the benchmark against which the ST-GNN's performance in capturing spatial and temporal dependencies within the building layout. Since the focus of this study was to develop and implement a new method to assess the impact of spatial relations on energy prediction using ST-GCN model, the other methods (e.g., XGBoost and LR) used in this study serve as baseline models without optimization or enhancements, so the impact of boundary conditions in XGBoost and LR models have not been considered. ST-GCN RMSE and MAPE results showed that the model did a good job predicting the hourly energy consumption, showcasing the importance of including space relationships in predicting the energy consumption. Root Mean Square Error (RMSE) for ST-GNN compared to XGBoost and LR across different seasons improved. The improvement of the ST-GNN occurred across all seasons, with a 33.85% reduction in RMSE during Summer, a 31.60% reduction in Fall, 36.77% reduction in Winter, and a 17.97% reduction in spring. These results emphasize the importance of considering spatial relationships in predicting energy consumption, and the research findings indicate that the ST-GCN model is robust and suitable for similar energy prediction applications. However, further studies are needed to assess the model's performance across a wider range of cases.

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

Author : Federico Divina
Publisher : MDPI
ISBN 13 : 3036508627
Total Pages : 100 pages
Book Rating : 4.0/5 (365 download)

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Book Synopsis Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast by : Federico Divina

Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Federico Divina and published by MDPI. This book was released on 2021-08-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.

Intelligent Optimization Modelling in Energy Forecasting

Author : Wei-Chiang Hong
Publisher : MDPI
ISBN 13 : 3039283642
Total Pages : 262 pages
Book Rating : 4.0/5 (392 download)

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Book Synopsis Intelligent Optimization Modelling in Energy Forecasting by : Wei-Chiang Hong

Download or read book Intelligent Optimization Modelling in Energy Forecasting written by Wei-Chiang Hong and published by MDPI. This book was released on 2020-04-01 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

On-line Building Energy Prediction Using Artificial Neural Networks

Author : Jin Yang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis On-line Building Energy Prediction Using Artificial Neural Networks by : Jin Yang

Download or read book On-line Building Energy Prediction Using Artificial Neural Networks written by Jin Yang and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Applications of Big Data and Artificial Intelligence in Smart Energy Systems

Author : Neelu Nagpal
Publisher : CRC Press
ISBN 13 : 1000963829
Total Pages : 318 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Applications of Big Data and Artificial Intelligence in Smart Energy Systems by : Neelu Nagpal

Download or read book Applications of Big Data and Artificial Intelligence in Smart Energy Systems written by Neelu Nagpal and published by CRC Press. This book was released on 2023-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth. Technical topics discussed in the book include: • Hybrid smart energy system technologies • Smart meters • Energy demand forecasting • Use of different protocols and communication in smart energy systems • Power quality and allied issues and mitigation using AI • Intelligent transportation • Virtual power plants • AI based smart energy business models • Smart home solutions • Blockchain solutions for smart grids.

Smart Energy Control Systems for Sustainable Buildings

Author : John Littlewood
Publisher : Springer
ISBN 13 : 3319520768
Total Pages : 281 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Smart Energy Control Systems for Sustainable Buildings by : John Littlewood

Download or read book Smart Energy Control Systems for Sustainable Buildings written by John Littlewood and published by Springer. This book was released on 2017-05-26 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is widespread interest in the way that smart energy control systems, such as assessment and monitoring techniques for low carbon, nearly-zero energy and net positive buildings can contribute to a Sustainable future, for current and future generations. There is a turning point on the horizon for the supply of energy from finite resources such as natural gas and oil become less reliable in economic terms and extraction become more challenging, and more unacceptable socially, such as adverse public reaction to ‘fracking’. Thus, in 2016 these challenges are having a major influence on the design, optimisation, performance measurements, operation and preservation of: buildings, neighbourhoods, cities, regions, countries and continents. The source and nature of energy, the security of supply and the equity of distribution, the environmental impact of its supply and utilization, are all crucial matters to be addressed by suppliers, consumers, governments, industry, academia, and financial institutions. This book entitled ‘Smart Energy Control Systems for Sustainable Buildings’ contains eleven chapters written by international experts based on enhanced conference papers presented at the Sustainability and Energy in Buildings International conference series. This book will be of interest to University staff and students; and also industry practioners.

Enhancing Future Skills and Entrepreneurship

Author : Kuldip Singh Sangwan
Publisher : Springer Nature
ISBN 13 : 3030442489
Total Pages : 281 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Enhancing Future Skills and Entrepreneurship by : Kuldip Singh Sangwan

Download or read book Enhancing Future Skills and Entrepreneurship written by Kuldip Singh Sangwan and published by Springer Nature. This book was released on 2020-07-27 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the proceedings of the 3rd Indo-German Conference on Sustainability in Engineering held at Birla Institute of Technology and Science, Pilani, India, on September 16–17, 2019. Intended to foster the synergies between research and education, the conference is one of the joint activities of the BITS Pilani and TU Braunschweig conducted under the auspices of Indo-German Center for Sustainable Manufacturing, established in 2009. The book is divided into three sections: engineering, education and entrepreneurship, covering a range of topics, such as renewable energy forecasting, design & simulation, Industry 4.0, and soft & intelligent sensors for energy efficiency. It also includes case studies on lean and green manufacturing, and life cycle analysis of ceramic products, as well as papers on teaching/learning methods based on the use of learning factories to improve students’problem-solving and personal skills. Moreover, the book discusses high-tech ideas to help the large number of unemployed engineering graduates looking for jobs become tech entrepreneurs. Given its broad scope, it will appeal to academics and industry professionals alike.

Artificial Intelligence

Author : Michael Negnevitsky
Publisher : Pearson Education
ISBN 13 : 9780321204660
Total Pages : 454 pages
Book Rating : 4.2/5 (46 download)

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Book Synopsis Artificial Intelligence by : Michael Negnevitsky

Download or read book Artificial Intelligence written by Michael Negnevitsky and published by Pearson Education. This book was released on 2005 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keeping the maths to a minimum, Negnevitsky explains the principles of AI, demonstrates how systems are built, what they are useful for and how to choose the right tool for the job.

Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)

Author : Zhaojun Wang
Publisher : Springer Nature
ISBN 13 : 9811395284
Total Pages : 1492 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019) by : Zhaojun Wang

Download or read book Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019) written by Zhaojun Wang and published by Springer Nature. This book was released on 2020-03-19 with total page 1492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019), with a focus on HVAC techniques for improving indoor environment quality and the energy efficiency of heating and cooling systems. Presenting inspiration for implementing more efficient and safer HVAC systems, the book is a valuable resource for academic researchers, engineers in industry, and government regulators.

Smart Meter Data Analytics

Author : Yi Wang
Publisher : Springer Nature
ISBN 13 : 9811526249
Total Pages : 306 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Smart Meter Data Analytics by : Yi Wang

Download or read book Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

Artificial Neural Network for Forecasting One Day Ahead of Global Solar Irradiance

Author : Hamid Ettayyebi
Publisher :
ISBN 13 :
Total Pages : 10 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Artificial Neural Network for Forecasting One Day Ahead of Global Solar Irradiance by : Hamid Ettayyebi

Download or read book Artificial Neural Network for Forecasting One Day Ahead of Global Solar Irradiance written by Hamid Ettayyebi and published by . This book was released on 2018 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to global warming, the world is seeking to use more renewable energy. In this study, we focus on solar energy, which has been receiving increased amounts of attention in the last few decades. The integration of solar energy into electricity networks requires reliable forecast information of solar resources enabling it to quantify the available energy and allowing it to optimally manage the transition between intermittent and conventional energies. Throughout our research, we investigated different forecasting techniques in order to find which one is appropriate for forecasting the daily global solar irradiance for the region of Rabat.The first-tested approach is linear modeling based on classical ARIMA-GARCH and exponential smoothing models. The second approach proposes non-linear modeling based on Artificial Neural Networks (ANNs) models. Numerous research has demonstrated the ability of ANNs to predict time series of weather data. In this study, we will examine a particular structure of ANNs, Multilayer Perceptron (MLP), which has been used the most among ANN structures in renewable energy and time series forecasting broadly. We used some statistical feature parameters to find the optimal structure of MLP in the univariate case and the multivariate case. The results showed that the MLP with exogenous variables performed better than the other models.