Long-term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions based on predictions...
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Long-term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions based on predictions help substantially reduce the power grid infrastructure costs. The classical approach of LTLF is limited to the usage of artificial neural networks (ANN) or regression-based approaches along with a large set of historical electricity load, weather, economy and population data. Considering the drawbacks of classical methods, this article introduces a novel sequence to sequence hybrid convolutional neural network and long short-term memory (CNN-LSTM) model to forecast the monthly peak load for a time horizon of three years. These drawbacks include, lack of sensitivity to changing trends over long time horizons, difficulty of fitting large number of variables and complex relationships, etc. (Velicer and Plummer, 1998). Forecasting time interval plays a key role in LTLF. Therefore, using monthly peak load avoids unnecessary complications while providing all essential information for a good long-term strategical planning. The accuracy of the proposed method is verified by the load data of "New South Wales (NSW)", Australia. The numerical results show that, proposed method has achieved higher prediction accuracy compared to the existing work on long-term load forecasting.
Software Defined Networking is an intelligent network management approach for monitoring and improving performance such as in cloud computing. These networks follow separation between the Forwarding layer and the Cont...
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ISBN:
(数字)9781665414906
ISBN:
(纸本)9781665414906
Software Defined Networking is an intelligent network management approach for monitoring and improving performance such as in cloud computing. These networks follow separation between the Forwarding layer and the Control layer in the system to enable efficient programmability to perform network configurations. The SDN Controller present in the network has complete information about the network schema and its components to update the routing information of the switches present. The dynamic nature of traffic in these networks with multi-layer switches hinder the efficiency of SDN's performance in multi-cloud environments. This paper proposes SDPredictNet, a Recurrent Neural Network framework deployed on the SDN Controller that can predict the traffic in the network and update flow tables of the higher layer switches to perform routing based on the perceived bottlenecks in the network. SDPredictNet uses a sequence-to-sequence model that trains on the network data congestion to forecast the traffic in the SDN which is then modelled by an Artificial Neural Network to predict the path of the packets. SDPredictNet has achieved a RMSE score of 0.07 and an accuracy of 99.88% for traffic estimation and subsequent path determination.
The stock market is a dynamic and volatile platform which provides an environment for traders to invest and trade in shares. The price of a stock is dependent on numerous static and dynamic features. Predicting the fu...
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ISBN:
(纸本)9781728196565
The stock market is a dynamic and volatile platform which provides an environment for traders to invest and trade in shares. The price of a stock is dependent on numerous static and dynamic features. Predicting the future price of a particular company's stock can be extremely beneficial for traders. Seq2Seq modelling helps map an input sequence to an output sequence. In this paper, we propose a system to predict the future Open, High, Close, Low (OHCL) value of a stock using a Bi-Directional LSTM based sequence to sequence modelling. Each OHCL price is an independent sequence and multitask learning helps map the interrelations between them. A multitask system is also proposed which uses sub tasks and shared tasks to model the prices. Stock prices of Tata Consumer Products Limited from the National Stock Exchange (NSE) of India is used. To evaluate the efficiency of the proposed systems, they are compared against various machine learning algorithms. The proposed Seq2Seq and multitask systems comfortably outperform the existing algorithms with RMSE values of 3.98 and 7.87 respectively.
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