The evolution of the smart grid has enabled residential users to manage the ever-growing energy d... more The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is fo...
Forecasting the behavior of the stock market is a classic but difficult topic, one that has attra... more Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable in...
The residential sector is a major contributor to the global energy demand. The energy demand for ... more The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak...
In this paper we investigate the complex relationship between tweet sentiments with the Nifty Sto... more In this paper we investigate the complex relationship between tweet sentiments with the Nifty Stock market Auto Index (esp. stock prices). We will analyze sentiments for nearly 1 million tweets from December 2015 to March 2016 for Nifty Auto Index which includes 15 auto industries stocks. Our results will show high correlation between stock prices and twitter sentiments. A Granger causality analysis and a Neural Network are then used to investigate the hypothesis that public mood states are predictive of changes in Nifty Auto Index closing values.
International Journal for Scientific Research and Development, 2013
Machine Learning is a growing field today in AI. We discuss use of a Supervised Learning algorith... more Machine Learning is a growing field today in AI. We discuss use of a Supervised Learning algorithm called as Regression Learning in this paper for ranking. Regression Learning is used as Prediction Model. The values of dependent variable are predicted by Regression Model based on values of Independent Variables. By Regression Learning if after Experience E, program improves its performance P, then program is said to be doing Regression Learning. We chose to use Linear Regression for Ranking and discuss approaches for Rank Regression Model Building by selecting best Ranking parameters from Knowledge and confirming their selection further by performing Regression Analysis during Model building. Example is explained. Analysis of Results and we discuss the Combined Regression and Ranking approach how it is better for enhancing use of Linear Regression for Ranking purpose. We conclude and suggesting future work Ranking and Regression.
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of mode... more Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance...
ABSTRACT Research has become synonymous with higher education. In the Indian context it has becom... more ABSTRACT Research has become synonymous with higher education. In the Indian context it has become necessary for economic growth in qualitative terms. Recently world over there is debate over the methodology of imparting education skills to students so as to make them capable of handling intricacies of real-life. Research in higher education, therefore, is dependent on the skills which student has been subjected to all throughout his academic life at school level and undergraduate level. Research outcomes in Indian institutions are still not matching with the industrial needs, innovations, and technological development, which ultimately fuel the socio- economic growth of the country. On the other hand, Indian industrial outcome on product designs, whether technical or social is also not up to the mark when it comes to international level. All this can be largely attributed to the research skills and attitude towards profession shown by the researchers. One important research skill one should therefore acquire is the development of ‘Problem Solving’ skill. This should be augmented with Techno-Managerial attitude when shaping the perspective on research problem. This paper is a humble attempt for giving a refreshing and integrating perspective on these skills, which stakeholders and higher education institutional leaders must reckon with.
The evolution of the smart grid has enabled residential users to manage the ever-growing energy d... more The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is fo...
Forecasting the behavior of the stock market is a classic but difficult topic, one that has attra... more Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable in...
The residential sector is a major contributor to the global energy demand. The energy demand for ... more The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak...
In this paper we investigate the complex relationship between tweet sentiments with the Nifty Sto... more In this paper we investigate the complex relationship between tweet sentiments with the Nifty Stock market Auto Index (esp. stock prices). We will analyze sentiments for nearly 1 million tweets from December 2015 to March 2016 for Nifty Auto Index which includes 15 auto industries stocks. Our results will show high correlation between stock prices and twitter sentiments. A Granger causality analysis and a Neural Network are then used to investigate the hypothesis that public mood states are predictive of changes in Nifty Auto Index closing values.
International Journal for Scientific Research and Development, 2013
Machine Learning is a growing field today in AI. We discuss use of a Supervised Learning algorith... more Machine Learning is a growing field today in AI. We discuss use of a Supervised Learning algorithm called as Regression Learning in this paper for ranking. Regression Learning is used as Prediction Model. The values of dependent variable are predicted by Regression Model based on values of Independent Variables. By Regression Learning if after Experience E, program improves its performance P, then program is said to be doing Regression Learning. We chose to use Linear Regression for Ranking and discuss approaches for Rank Regression Model Building by selecting best Ranking parameters from Knowledge and confirming their selection further by performing Regression Analysis during Model building. Example is explained. Analysis of Results and we discuss the Combined Regression and Ranking approach how it is better for enhancing use of Linear Regression for Ranking purpose. We conclude and suggesting future work Ranking and Regression.
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of mode... more Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance...
ABSTRACT Research has become synonymous with higher education. In the Indian context it has becom... more ABSTRACT Research has become synonymous with higher education. In the Indian context it has become necessary for economic growth in qualitative terms. Recently world over there is debate over the methodology of imparting education skills to students so as to make them capable of handling intricacies of real-life. Research in higher education, therefore, is dependent on the skills which student has been subjected to all throughout his academic life at school level and undergraduate level. Research outcomes in Indian institutions are still not matching with the industrial needs, innovations, and technological development, which ultimately fuel the socio- economic growth of the country. On the other hand, Indian industrial outcome on product designs, whether technical or social is also not up to the mark when it comes to international level. All this can be largely attributed to the research skills and attitude towards profession shown by the researchers. One important research skill one should therefore acquire is the development of ‘Problem Solving’ skill. This should be augmented with Techno-Managerial attitude when shaping the perspective on research problem. This paper is a humble attempt for giving a refreshing and integrating perspective on these skills, which stakeholders and higher education institutional leaders must reckon with.
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