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Artificial neural networks (ANN) are among the nonlinear prediction techniques popular in the last two decades. Recent studies show that ANN can be modeled with different training techniques. ANN is usually trained by the backpropagation method (BP). In this study, ANN structures were trained by using artificial bee colony algorithm (ABC) and, weight and bias values were tried to be determined. ABC...
Energy forecast is essential for a good planning of the electricity consumption as well as for the implementation of decision support systems which can lead the decision making process of energy system. Energy consumption time series prediction problems represent a difficult type of predictive modelling problem due to the existence of complex linear and non-linear patterns. This paper presents two...
In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian Process (GP) model combined with Linear Discriminate Analysis (LDA) as dimensionality reduction method is proposed. To evaluate the proposed approach, its performance is assessed using three scenarios: long window (latest 50 variables), short window (latest 5 variables) and persistence. To evaluate...
Everywhere in the world tax revenues are rolled back for the commonwealth to invest and finance goods and public services, such as: infrastructure, health, security and education. The predict income revenue (taxes) is one of the challenges that the Secretariat of the Federal Revenue of Brazil (RFB for its Portuguese acronym) has. This is an important challenge since the obtained information is valuable...
Load forecasting is a fundamental task for the planning, operation and exploration of the Electric Power Systems. The importance of forecasting has become more evident with the restructuring of the national energy sector and the creation of projects linked to smart grids, namely in Portugal — o InovGrid. This paper describes the computational forecast model of the Daily Load Diagram based on the Levenberg-Marquardt...
The paper presents a mathematical model of processing and forecasting time series data. The mathematical model is based on the methods of artificial neural networks and preliminary data processing using wavelet transform. Various classes of algorithms for predicting changes in the parameters of continuous functions and time series that occur in the interval called the prediction horizon are considered...
In this paper we consider a hybrid approach to forecasting time series using neuro-fuzzy prediction models and Fuzzy Cognitive Maps. Main idea of proposed approach is hybridization two different ways for time series forecasting. We can make quantitative and qualitative forecast. In addition, we describes the different approaches to learning and optimization of the network, such as the methods of particle...
The Fuzzy Nearest Neighbor time series forecasting technique (FNN), compiles a time series into a set of fuzzy rules capable of inferring the next value, based on the current observation. The training phase compiles all different scenarios of what has been observed in the past as a set of fuzzy rules. FNN has been tested against other methodologies, showing a satisfactory performance. This article...
The forecast of Singapore condominium prices is important for potential buyers to make informed decisions. This paper applies two algorithms to predict Singapore housing market and to compares the predictive performance of artificial neural network (ANN) model, i.e., the multilayer perceptron, with autoregressive integrated moving average (ARIMA) model. The more superior model is used to predict the...
Increased penetration of renewable energy based generators throughout modern distribution networks makes it crucial to seek elevated levels of accuracy in forecasting methods. This paper presents a new load forecasting method for residential distribution feeders. It uses, load time series decomposition to distinguish between all types of loads and events on feeder. Then, a generalized regression neural...
Monitoring crop areas is a key issue in remote sensing studies. A Crop Proportion Phenology Index (CPPI) model has previously been developed for estimation of winter wheat areas. Here we test the CPPI model in different areas using remote sensing data for varied kernel functions, including linear regression (LR), Artificial Neural Network (ANN), and Support Vector Regression (SVR). The differences...
One of the purposes of smart grids is the efficient delivery of sustainable, economic and secure electricity supplies. One of the strategies used for this purpose is the control and improvement of overhead lines ampacity. A smart use of the actual ampacity requires the implementation of intelligent control devices. Research on ampacity is aimed not only to calculate it in the real time, but also to...
The accuracy of electric load forecasts significantly affects the overall performance of power system. Some time due to complicated load pattern, forecasting becomes difficult. The object of this study is to develop more effective forecasting models, among others. This paper compares the electric load forecasting accuracy of ANN based techniques. This study investigates the time series techniques...
In recent years, double auctions have become a topic of much interest in both economics and computer science literature, and automated trading has become very popular in stock markets. In this context, although many time series prediction studies are focused in the prediction of exact values in the future, evidences show that this kind of problem perform better when we transform it into a classification...
If the one-step forecasting of a time series is already a challenging task, performing multi-step ahead forecasting is more difficult. Several approaches that deal with this complex problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, combination of both the recursive and direct strategies, called DirREC, the Multi-Input Multi-Output (MIMO) strategy, and the...
The increasing shares of renewable energy sources at low voltage distribution nodes are the cause of increased operational uncertainty. This uncertainty must also be taken into account during operational planning for the short term period, i.e. up to five days ahead. Therefore the system operators must take into account how low-voltage load as well as generation change at each node in the near future...
Parkinson's disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients' responses to drug therapies and lead to better-targeted...
Accurate machine performance prediction is crucial to an effective maintenance strategy for improved reliability and to reduce total maintenance cost. In this study, a time series neural network based approach is introduced to achieve more accurate and reliable performance prediction of machine using condition monitoring data source. The proposed time series model utilizes the various measured condition...
In this paper, Kalman Fusion algorithm is applied to combine outputs of three forecasting engines which are used to predict electricity price signal of the Spanish electricity market. Employed engines which are Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Autoregressive Moving Average (ARMA), are all powerful and popular kinds of time series models. After applying...
Prediction of seasonal influenza epidemics is certainly a forming and effective step towards taking appropriate preventive actions. Improvement on public informing, decreasing the number of infected cases, undesirable effects and deaths due to influenza and also increasing vigilance of Iranian Influenza Surveillance System (IISS), have been practical goals of this research. A forecasting system has...
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