The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Hassan Elragal - One of the best experts on this subject based on the ideXlab platform.
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A neuro-fuzzy approach to short-term load forecasting in a price-Sensitive Environment
IEEE Transactions on Power Systems, 2002Co-Authors: Alireza Khotanzad, Enwang Zhou, Hassan ElragalAbstract:This paper presents a new approach to short-term load forecasting in a deregulated and price-Sensitive Environment. A real-time pricing type scenario is envisioned where energy prices could change on an hourly basis with the consumer having the ability to react to the price signal through shifting his electricity usage from expensive hours to other times when possible. The load profile under this scenario would have different characteristics compared to that of the regulated, fixed-price era. Consequently, short-term load forecasting models customized on price-inSensitive (PIS) historical data of regulated era would no longer be able to perform well. In this work, a price-Sensitive (PS) load forecaster is developed. This forecaster consists of two stages, an artificial neural network based PIS load forecaster followed by a fuzzy logic (FL) system that transforms the PIS load forecasts of the first stage into PS forecasts. The first stage forecaster is a widely used forecaster in industry known as ANNSTLF. For the FL system of the second stage, a genetic algorithm based approach is developed to automatically optimize the number of rules and the number and parameters of the fuzzy membership functions. Another FL system is developed to simulate PS load data from the PIS historical data of a utility. This new forecaster termed NFSTLF is tested on three PS database and it is shown that it produces superior results to the PIS ANNSTLF.
Alireza Khotanzad - One of the best experts on this subject based on the ideXlab platform.
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A neuro-fuzzy approach to short-term load forecasting in a price-Sensitive Environment
IEEE Transactions on Power Systems, 2002Co-Authors: Alireza Khotanzad, Enwang Zhou, Hassan ElragalAbstract:This paper presents a new approach to short-term load forecasting in a deregulated and price-Sensitive Environment. A real-time pricing type scenario is envisioned where energy prices could change on an hourly basis with the consumer having the ability to react to the price signal through shifting his electricity usage from expensive hours to other times when possible. The load profile under this scenario would have different characteristics compared to that of the regulated, fixed-price era. Consequently, short-term load forecasting models customized on price-inSensitive (PIS) historical data of regulated era would no longer be able to perform well. In this work, a price-Sensitive (PS) load forecaster is developed. This forecaster consists of two stages, an artificial neural network based PIS load forecaster followed by a fuzzy logic (FL) system that transforms the PIS load forecasts of the first stage into PS forecasts. The first stage forecaster is a widely used forecaster in industry known as ANNSTLF. For the FL system of the second stage, a genetic algorithm based approach is developed to automatically optimize the number of rules and the number and parameters of the fuzzy membership functions. Another FL system is developed to simulate PS load data from the PIS historical data of a utility. This new forecaster termed NFSTLF is tested on three PS database and it is shown that it produces superior results to the PIS ANNSTLF.
Enwang Zhou - One of the best experts on this subject based on the ideXlab platform.
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A neuro-fuzzy approach to short-term load forecasting in a price-Sensitive Environment
IEEE Transactions on Power Systems, 2002Co-Authors: Alireza Khotanzad, Enwang Zhou, Hassan ElragalAbstract:This paper presents a new approach to short-term load forecasting in a deregulated and price-Sensitive Environment. A real-time pricing type scenario is envisioned where energy prices could change on an hourly basis with the consumer having the ability to react to the price signal through shifting his electricity usage from expensive hours to other times when possible. The load profile under this scenario would have different characteristics compared to that of the regulated, fixed-price era. Consequently, short-term load forecasting models customized on price-inSensitive (PIS) historical data of regulated era would no longer be able to perform well. In this work, a price-Sensitive (PS) load forecaster is developed. This forecaster consists of two stages, an artificial neural network based PIS load forecaster followed by a fuzzy logic (FL) system that transforms the PIS load forecasts of the first stage into PS forecasts. The first stage forecaster is a widely used forecaster in industry known as ANNSTLF. For the FL system of the second stage, a genetic algorithm based approach is developed to automatically optimize the number of rules and the number and parameters of the fuzzy membership functions. Another FL system is developed to simulate PS load data from the PIS historical data of a utility. This new forecaster termed NFSTLF is tested on three PS database and it is shown that it produces superior results to the PIS ANNSTLF.
Eugene Murphy - One of the best experts on this subject based on the ideXlab platform.
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wastewater disposal option in mundaring a practical case study
Desalination, 1996Co-Authors: Eugene MurphyAbstract:Urban development in the Hills areas of Perth is difficult to provide with local or regional wastewater systems due to the remoteness and isolation as well as the Sensitive Environment. Development to date has therefore mainly been on the basis of large-sized lots with individual on-site systems. This in turn puts limitations on further development or re-development of these areas. In some locations such as Kalamunda, a connection to one of the Authority's major schemes has recently been possible. In other areas such as Mundaring, a local scheme would be required. This paper describes the development of such a scheme to the concept stage for the development of the Mundaring town centre.
Arunabha Sen - One of the best experts on this subject based on the ideXlab platform.
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Coverage and connected coverage problems for sensors embedded in a temperature-Sensitive Environment
International Journal of Sensor Networks, 2010Co-Authors: Arunabha Sen, Nibedita Das, Sudheendra MurthyAbstract:Several issues are encountered during deployment of bio-sensors in a human or animal body. Radio transmitters during operation dissipate energy and raise the temperature of its surroundings. A temperature-Sensitive Environment, such as the human body, can tolerate such increase in temperature only up to a certain threshold value, beyond which serious injury may result. To avoid such injury, the sensor placement must be carried out in a way that ensures the surrounding area temperature remains within the threshold. Using a thermal model for heat distribution from multiple heat sources (radio transmitters), we observed that if the sensor nodes are placed sufficiently apart from each other, then the temperature of the surrounding area does not exceed the threshold. This minimum separation distance constraint gives rise to a new variation of the sensor coverage problem.
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GLOBECOM - Approximation Algorithm for Avoiding Hotspot Formation of Sensor Networks for Temperature Sensitive Environments
GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009Co-Authors: Nibedita Das, Pavel Ghosh, Arunabha SenAbstract:Sensing and transmission phenomena of an implanted sensor dissipates energy which results in rise in temperature of its surroundings. Simultaneous operation of such multiple active sensors increases the temperature of the surrounding Environment causing hotspots. Such hotspots are highly undesirable as they may cause damage to the Environment as well as to the sensor network, posing a challenge for deployment of sensors. The problem is further enhanced for a temperature Sensitive Environment, as the allowable threshold temperature for such Environments is less. Here we investigate the formation of hotspots in such temperature Sensitive Environments due to the heat dissipation of multiple active sensors and try to achieve a maximum coverage of such networks avoiding hotspots. We formulate this as a variation of the maximum independent set problem for hypergraphs. We devise an Integer Linear Program to achieve the optimal solution for the problem. We also provide a greedy heuristic solution for the problem. For a special case of this problem, where the hotspots are formed due to pairs of sensors only, we prove a 5-approximation bound for the greedy solution. Experimental results show that our algorithm achieves near-optimal solutions in almost all the test cases.
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SECON - Coverage Problem for Sensors Embedded in Temperature Sensitive Environments
2007 4th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, 2007Co-Authors: Arunabha Sen, Sudheendra Murthy, Nibedita Das, Ling Zhou, Bao Hong Shen, P. BhattacharyaAbstract:The coverage and connectivity problem in sensor networks has received significant attention of the research community in the recent years. In this paper, we study this problem for sensors deployed in temperature Sensitive Environments. This paper is motivated by the issues encountered during deployment of bio-sensors in a human/animal body. Radio transmitters during operation dissipate energy and raise the temperature of its surroundings. A temperature Sensitive Environment like the human body can tolerate such increase in temperature only up to a certain threshold value, beyond which serious injury may occur. To avoid such injuries, the sensor placement must be carried out in a way that ensures the surrounding temperature to remain within the threshold. Using a thermal model for heat distribution from multiple heat sources (radio transmitters), we observed that if the sensor nodes are placed sufficiently apart from each other, then the temperature of the surrounding area does not exceed the threshold. This minimum separation distance constraint gives rise to a new version of the sensor coverage problem that has not been studied earlier. We prove that both the optimization version and the feasibility version of the new problem are NP-complete. We further show that an epsiv-approximation algorithm for the problem cannot exist unless P = NP. We provide two heuristic solutions for the problem and evaluate the efficacy of these solutions by comparing their performances against the optimal solution. The simulation results show that our heuristic solutions almost always find near optimal solution in a fraction of the time needed to find the optimal solution. Finally, an algorithm for forming a connected sensor network with minimum transmission power in such a scenario is provided.
