Changing user behavior with home electricity use to reduce and shift the demand on the electric grid

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 84-85).Most household consumers in the US are unaware of their electrical usage or the price of electricity until they receive their monthly bill. However, they are concerned about being "wasteful" when it comes to electricity use. But most consumers have no idea what being wasteful means. What if there was a way to show residential consumers their real-time electrical usage in terms of price or in terms of environmental emissions from non-renewable power plants? Would this appeal to their concerns about wastefulness and cause them to change their behavior with household appliance use? To test this question an experiment was designed, and multiple prototypes were built. The experiment consisted of a prototype showing a traffic light color pattern of two lights. The first light indicated the price/emissions metric chosen based on user allegiance. The second light indicated the usage of electricity in their home.After running this experiment, the key takeaway was that that consumers will change their electrical usage behavior based on a metric that matters to them but will not compromise comfort or convenience over price or emissions. Electric energy trading in the US is a complicated system and fundamentally a business. Electrical energy is predicted and traded the day before, generally using clean energy sources in the US. However, if there is a surge in demand on the predicted day, dirty power is turned on. A dirty power plant is classified as being harmful to the environment by burning coal or oil. Dirty power plants are also expensive to the consumer and inefficient in the electric grid but can be turned on instantly in times of need. Because of this, the trading system is designed to minimize the use of dirty power. The electricity trading models follow a principle called the "duck curve".The duck curve is a graph of power production over the course of a day that shows the timing imbalance between peak demand and renewable energy production. Generally, grid usage follows the duck curve.by Saluka Amarasinghe.S.M. in Engineering and ManagementS.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Progra

Simulation of Regression Analysis by an Automated System utilizing Artificial Neural Networks

Artificial Neural Networks have been gaining popularity as statistical tools since it resolves some disadvantages of conventional regression analysis techniques. This paper describes the implementation issues on designing dynamically changing artificial neural networks which are to be applied for the situations where the Regression Analysis is to be used. Furthermore, in order to resolve some of the problems of existing statistical packages like MINITAB, R and SAS, a computer based analysis system is proposed in order to simulate the complete process of building up a regression model and to make future predictions. When implementing the automated system, we used JAVA which supports Object Oriented Programming and MATLAB for easy calculation of mathematical functions. Finally we present a comparative study on the results obtained by the proposed system and the conventional statistical methods. This system provides better output in identifying relationships between independent and dependent variables compared to conventional regression techniques

Fuzzy based binary feature profiling for modus operandi analysis

It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level