BACKGROUND & OBJECTIVES Japanese encephalitis (JE) is one of the dreaded mosquito-borne viral diseases mostly prevalent in south Asian countries including India. Early warning of the disease in terms of disease intensity is crucial for taking adequate and appropriate intervention measures. The present study was carried out in Dibrugarh district in the state of Assam located in the northeastern ...

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such fashion that inter-dependencies among several different can be exploited used for forecasting purposes by filtering joining their samples. resulting sum...

This project is focused on the design of price forecasting tools for market operators and for market traders, taking into account the distinct purposes, data availability, and time horizons of the distinct users. Empirical price data from the MISO and RTE have been analyzed. A combined model is developed to forecast MISO day-ahead nodal prices. An ARIMA model is constructed to forecast RTE week...

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Ei...

Operational flare forecasting aims at providing predictions that can be used to make decisions, typically on a daily scale, about the space weather impacts of occurrence. This study shows video-based deep learning for operational purposes when training and validation sets network optimization are generated while accounting periodicity solar cycle. Specifically, this article describes an algorit...

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based ...

Accurate forecasting of short-term passenger flow has been one of the most important issues in urban rail transit planning and operation. Considering the shortcomings of traditional forecasting methods, and in order to improve forecasting accuracy of passenger flow, this paper presents a wavelet neural network (WNN) for short-term passenger flow forecasting. One real urban rail transit station ...

We introduce a data-driven forecasting method for high dimensional, chaotic systems using Long-Short Term Memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high dimensional dynamical systems in their reduced order space and are shown to be an effective set of non-linear approximators of their attractor. We demonstrate the forecasting performance of ...

With the rapid growth of power system and the increase in their complexity of the networks, load forecasting plays a vital role in economic operation of power systems, network planning and infrastructure development. Electricity demand forecasting is concerned with the prediction of a very short term, short term, medium term and long term load demand, depending on the time horizon. This paper p...

Background and Objectives: Efficient cost management in hospitals’ pharmaceutical inventories have the potential to remarkably contribute to optimization of overall hospital expenditures. To this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. While the linear methods are frequently used for forecasting purposes chiefly due to their si...