Real-Time Water Demand Forecasting System through an Agent-Based Architecture

Water policies have evolved enormously since the Rio Earth Summit (1992). These changes have led to the strategic importance of Water Demand Management. The aim is to provide wa-ter where and when it is required using the fewest resources. A key variable in this process is the demand forecasting. It is not sufficient to have long term forecasts, as the current context requires the continuous availability of reliable hourly predictions. This paper incorporates arti-ficial intelligence to the subject, through an agent-based system, whose basis are complex fore-casting methods (Box-Jenkins, Holt-Winters, Multi-Layer Perceptron Networks and Radial Ba-sis Function Networks). The prediction system also includes data mining, oriented to the pre and post processing of data and to the knowledge discovery, and other agents. Thereby, the system is capable of choosing at every moment the most appropriate forecast, reaching very low errors. It significantly improves the results of the different methods separatelyAyuda predoctoral Severo Ochoa. Ref BP13011

Forecasting Automobile Demand Via Artificial Neural Networks & Neuro-Fuzzy Systems

The objective of this research is to obtain an accurate forecasting model for the demand for automobiles in Iran\u27s domestic market. The model is constructed using production data for vehicles manufactured from 2006 to 2016, by Iranian car makers. The increasing demand for transportation and automobiles in Iran necessitated an accurate forecasting model for car manufacturing companies in Iran so that future demand is met. Demand is deduced as a function of the historical data. The monthly gold, rubber, and iron ore prices along with the monthly commodity metals price index and the Stock index of Iran are Artificial neural network (ANN) and artificial neuro-fuzzy system (ANFIS) have been utilized in many fields such as energy consumption and load forecasting fields. The performances of the methodologies are investigated towards obtaining the most accurate forecasting model in terms of the forecast Mean Absolute Percentage Error (MAPE). It was concluded that the feedforward multi-layer perceptron network with back-propagation and the Levenberg-Marquardt learning algorithm provides forecasts with the lowest MAPE (5.85%) among the other models. Further development of the ANN network based on more data is recommended to enhance the model and obtain more accurate networks and subsequently improved forecasts

Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few

Finding kernel function for stock market prediction with support vector regression

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction

Modeling Dissolved Oxygen (DO) Concentration Using Different Neural Network Techniques

The concentration of dissolved oxygen (DO) is important for the healthy functioning of aquatic ecosystems, and a significant indicator of the state of aquatic ecosystems. DO is a parameter frequently used to evaluate the water quality on different reservoirs and watersheds.In this study, two different ANN models, that is, the multilayer perceptron (MLP) and radial basis neural network (RBNN), were developed to estimate DO concentration by using various combinations of daily input variables, pH, discharge (Q), temperature (T), and electrical conductivity (EC) measured by U.S. Geological Survey (USGS). The data of Fountain Creek Stream - Gauging Station (USGS Station No: 07106000) which cover 18 years daily data between 1994-2011 were used. The ANN results were compared with those of the multiple linear regression (MLR). Comparison of the results indicated that the MLP and RBNN performed better than the MLR model. The RBNN model with three inputs which are pH, Q,and T was found to be the best model in estimation of DO concentration according to the root mean square error, mean absolute error and determination coefficient (R2) criteria

Comparison of Two Types of Artificial Neural Networks for Predicting Failure Frequency of Water Conduits

This paper presents the results of a comparison between two artificial neural network structures, i.e. the multilayer perceptron and the ANN with radial basis functions, with regard to the prediction of the failure intensity (failure rate) indicator for water mains, distribution pipes and house connections. The artificial neural network architecture included seven input signals (the number of house connections, the length of water mains, distribution pipes and house connections and the number of their failures). There were three neurons (the failure frequency indicators for the three types of conduits) at the ANN’s output. Operating data from the years 1999-2013 were used to train the ANNs while the optimal model was verified using data from the year 2014. Two models (MLP 7-14-3 and RBF 7-4-3), characterized by the best agreement between the predicted results and the experimental ones, were selected from a few tens of models. The RBF ANNs would generate results showing poorer agreement with the experimental failure frequency indicator values

Development of a smart grid for the proposed 33 KV ring main Distribution System in NIT Rourkela

The non-reliability of fossil fuels has forced the world to use energy efficiently. These days, it is being stressed to use the electrical power smartly so that energy does not go waste. And hence comes the concept of a Smart Grid. So it becomes necessary for reputed places of academics to develop the prototype of the same in their campus. National Institute of Technology (NIT) Rourkela intends to set up a 33KV Ring Main Distribution System including 33/0.433 KV substations in its campus. The present 11KV line will be discarded and replaced by the 33KV system. The main driving force behind this step by the management is to accommodate the stupendously increased power requirement of the institute. The above mentioned plan also includes, set up of Data Acquisition System (DAS) that intends to monitor the electrical equipment in the substations. This is being done not only to increase the accountability and reliability of the distribution system but also to encourage academic research in the distribution automation domain. All in all, an excellent step towards make the Grid, Smart. In this project work the focus is laid on getting load flow solution of the 33KV ring main system. Here the authors use a specialized algorithm for distribution network with high R/X value to obtain the load flow solution. Then using artificial neural networks computation, algorithms are implemented to do the load forecasting and dynamic tariff setting. At the end a Web Portal, the NITR e-Power Monitoring System is developed that will be an excellent interface to the public in general and will help the students of the institute to know their grid well. In short a conscious effort is put to make the grid more interactive

Analysing and forecasting tourism demand in Vietnam with artificial neural networks

Mestrado APNORVietnam has experienced a tourism boom over the last decade with more than 18 million international tourists in 2019, compared to 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and income for the tourism sector, making it the key driver to the socio-economic development of the country. Facing the COVID-19 pandemic, Vietnam´s tourism has suffered extreme economic losses. However, the number of international tourists is expected to reach the pre-pandemic levels in the next few years after the COVID-19 pandemic subsides. Forecasting tourism demand plays an essential role in predicting future economic development. Accurate predictions of tourism volume would facilitate decision-makers and managers to optimize resource allocation as well as to balance environmental and economic aspects. Various methods to predict tourism demand have been introduced over the years. One of the most prominent approaches is Artificial Neural Network (ANN) thanks to its capability to handle highly volatile and non-linear data. Given the significance of tourism to the economy, a precise forecast of tourism demand would help to foresee the potential economic growth of Vietnam. First, the research aims to analyse Vietnam´s tourism sector with a special focus on international tourists. Next, several ANN architectures are experimented with the datasets from 2008 to 2020, to predict the monthly number of international tourists traveling to Vietnam including COVID-19 lockdown periods. The results showed that with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can forecast the number of international tourists for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam´s policymakers and firm managers to make better investment and strategic decisions to promote tourism after the COVID-19 situation.O Vietname conheceu um boom turístico na última década com mais de 18 milhões de turistas internacionais em 2019, em comparação com 1,5 milhões há vinte e cinco anos. As despesas turísticas traduziram-se num aumento do emprego e de receitas no sector do turismo, tornando-o no principal motor do desenvolvimento socioeconómico do país. Perante a pandemia da COVID-19, o turismo no Vietname sofreu perdas económicas extremas. Porém, espera-se que o número de turistas internacionais, pós pandemia da COVID-19, atinja os níveis pré-pandémicos nos próximos anos. A previsão da procura turística desempenha um papel essencial na previsão do desenvolvimento económico futuro. Previsões precisas facilitariam os decisores e gestores a otimizar a afetação de recursos, bem como o equilíbrio entre os aspetos ambientais e económicos. Vários métodos para prever a procura turística têm sido introduzidos ao longo dos anos. Uma das abordagens mais proeminentes assenta na metodologia das Redes Neuronais Artificiais (ANN) dada a sua capacidade de lidar com dados voláteis e não lineares. Dada a importância do turismo para a economia, uma previsão precisa da procura turística ajudaria a prever o crescimento económico potencial do Vietname. Em primeiro lugar, a investigação tem por objetivo analisar o sector turístico do Vietname com especial incidência nos turistas internacionais. Em seguida, várias arquiteturas de ANN são experimentadas com um conjunto de dados de 2008 a 2020, para prever o número mensal de turistas internacionais que se deslocam ao Vietname, incluindo os períodos de confinamento relacionados com a COVID-19. Os resultados mostraram, com a correta seleção de arquiteturas ANN e dados dos 12 meses anteriores, os melhores modelos ANN podem prever o número de turistas internacionais para o próximo mês com uma MAPE entre 7,9% e 9,2%. Como o método evidenciou a sua precisão de previsão, o mesmo pode servir como uma ferramenta valiosa para os decisores políticos e gestores de empresas do Vietname, pois irá permitir fazer melhores investimentos e tomarem decisões estratégicas para promover o turismo pós situação da COVID-19

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON