Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1976 / Bibliography: leaves 115-121. / by Frederic S. Mishkin. / Ph. D. / Ph. D. Massachusetts Institute of Technology, Department of Economics

abstract: The grand transition of electric grids from conventional fossil fuel resources to intermittent bulk renewable resources and distributed energy resources (DERs) has initiated a paradigm shift in power system operation. Distributed energy resources (i.e. rooftop solar photovoltaic, battery storage, electric vehicles, and demand response), communication infrastructures, and smart measurement devices provide the opportunity for electric utility customers to play an active role in power system operation and even benefit financially from this opportunity. However, new operational challenges have been introduced due to the intrinsic characteristics of DERs such as intermittency of renewable resources, distributed nature of these resources, variety of DERs technologies and human-in-the-loop effect. Demand response (DR) is one of DERs and is highly influenced by human-in-the-loop effect. A data-driven based analysis is implemented to analyze and reveal the customers price responsiveness, and human-in-the-loop effect. The results confirm the critical impact of demographic characteristics of customers on their interaction with smart grid and their quality of service (QoS). The proposed framework is also applicable to other types of DERs. A chance-constraint based second-order-cone programming AC optimal power flow (SOCP-ACOPF) is utilized to dispatch DERs in distribution grid with knowing customers price responsiveness and energy output distribution. The simulation shows that the reliability of distribution gird can be improved by using chance-constraint. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

With the growing concerns about energy shortage and demand supply imbalance, demand side management (DSM) activities has found its way into the mining industry. This study analyzes the potential to save energy and energy-costs in underground mine ventilation networks, by application of DSM techniques. Energy saving is achieved by optimally adjusting the speed of the main fan to match the time-varying flow demand in the network, which is known as ventilation on demand (VOD). Further cost saving is achieved by shifting load to off-peak or standard times according to a time of use (TOU) tariff, i.e. finding the optimal mining schedule. The network is modelled using graph theory and Kirchhoff’s laws; which is used to form a non-linear, constrained, optimization problem. The objective of this problem is formulated to minimize the energy cost; and hence it is directly given as a function of the fan speed, which is the control variable. As such, the operating point is found for every change in the fan speed, by incorporating the fan laws and the system curve. The problem is solved using the fmincon solver in Matlab’s optimization toolbox. The model is analyzed for different scenarios, including varying the flow rate requirements and tariff structure. Although the results are preliminary and very case specific, the study suggests that significant energy and energy-cost saving can be achieved in a financially viable manner. / Dissertation (MEng)--University of Pretoria, 2014. / tm2015 / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Acceptance and Commitment Training (ACT) is a psychological intervention that combines acceptance and mindfulness approaches with behavior change strategies to increase psychological flexibility, defined as present moment awareness despite unpleasant private events, and behaving based on personal values. Obesity is a socially significant problem effecting duration and quality of life. Quantifying differences between high and low exercise groups and evaluating interventions that affect exercise value represent an avenue to understand obesity and change behavior. This study assessed the effects of a brief ACT exercise on the demand for general and sport-specific exercise and delay discounting rates of four commodities (i.e., money, food, general exercise, and sport-specific exercise) in Division I athletes (n=78) and non-athletes (n=78) from the same university. Statistically significant main effects for general exercise were observed between athletes compared to non-athletes for intensity, elasticity, and breakpoint. Differences in demand for sport-specific exercise between athletes and non-athletes were observed across all behavioral economic indices. The ACT intervention did not alter the demand for general or sport-specific exercise. General and sport-specific exercise were valued similarly between individual participants in each group. In the delay discounting task, ACT significantly decreased degree of discounting of all four commodities (increased the area under the curve) in both athletes and non-athletes. No differences were observed between athletes and non-athletes for any of the four commodities, indicating that athletes and non-athletes discounted these commodities similarly. Results from the demand task provide initial support for the extension of hypothetical purchase tasks to exercise behavior. which would offer a standardized method to quantify exercise value efficiently. Results from the discounting task provide initial evidence indicating that ACT may be effective in reducing discounting rates across commodities. Given that steep discounting rates are considered to play an important causal role in maladaptive behaviors related to obesity, evidence of a therapeutic intervention with the potential to produce longer-lasting change in decision-making is encouraging.

With the rapid adoption of electric vehicles and the rise of power generation from re- newable sources, intelligent management of power demand on a household level is gaining importance. Current algorithms used for that purpose have negative privacy implications and focus only on controlling the charging of electric vehicles while ignoring other ap- pliances. We describe a decentralized algorithm designed to control the power demand of different types of household appliances along with the charging of electric vehicles while preserving the privacy of the subscribers. We also present a smart grid simulator to evaluate the algorithm's effectiveness along with results of simulating a scale model of the power grid of the state of Texas. 1

Abstract When the covid virus spread and took a major grip on society around the world, it was classified as a pandemic and the Swedish Public Health Agency gave society advice and recommendations that those who could work from home should do so as much as possible. When advice and recommendations ceased, employees must return to the workplace again. Many employers today offer new distance contracts so that they can offer employees continued flexible forms of work, such as working remotely. The purpose of this study is to create a deeper understanding of how women who work in an authority experience the return to the workplace after working remotely for over a year. To achieve the purpose of the following questions: How do female administrators feel about partially returning to the workplace after full-time distance work for a longer period of time? What challenges and opportunities are experienced by partially returning to the workplace?For this qualitative interview study, six semi-structured interviews were conducted where the interviewees would: (1) work at the authority, (2) have worked remotely for a total of at least one year during the pandemic and (3) be women. The collected empirical data has been further analyzed with the help of Karasek & Theorell's Requirements - control and support model, previous research on the balance between private and working life and previous research on flexibility. The results of the study show that the interviewees experience both challenges such as adjusting their days and returning to their old (and new) routines, the logistics in the form of travel time to and from the workplace and the work requirements that are added in the workplace and opportunities such as missing social interactions and continue to be able to use telework, which is seen as the best of both worlds where it is possible to combine working from home and being in the office.

The building sector is responsible for approximately 40 % of the total energy usage in Sweden. In office buildings the heating, ventilation and air conditioning system can account for up to 55 % of the energy usage. In order to reduce the energy usage of the heating, ventilation and air conditioning system different control methods are often used. One of these control methods is demand controlled ventilation, where the ventilation system is controlled with regard to occupancy with the help of motion and/or CO2 sensors. The aim of this thesis was to determine the energy savings potential of a heat recovery unit as well as demand controlled ventilation in an office building. The effect of longer intervals between sensor control signals to the ventilation system was also investigated. This is done by creating schedules, gathered from actual building occupancy, that are being used to control the occupancy and ventilation in a building model in the building performance simulation software IDA ICE. As a reference building, the fifth floor of the LU1 section of the natural science building at Umeå University is used. The reference building consists of 40 offices for which the occupancies are known. The average occupancy for all the offices combined throughout the investigated time period is determined to be 34.8 %. The results from the simulations indicate that an energy savings potential of 52.98 % can be achieved by a heat recovery unit with an efficiency of 80 % or 95 %, when compared to not having a heat recovery unit. When implementing demand controlled ventilation an energy savings potential of 2.8-11.0 % can be achieved, with the energy savings potential decreasing when the efficiency of the heat recovery unit increases. Finally, it is shown that longer intervals between sensor control signals to the ventilation system leads to a small increase in energy usage and poorer indoor air quality.

The primary aim of this research was to assess the adequacy of postexperimental inquiries (PEI) used in deception research, as well as to examine whether mood state, reward, or administering the PEI as a face-to-face interview or computer survey impacts participants' willingness to divulge suspicion or knowledge about a study. We also sought to determine why participants are not always forthcoming on the PEI. Study 1 examined how frequently PEIs are included in research and found that most researchers employing deception do use a PEI. Studies 2 and 3 showed that participants are often unwilling to divulge suspicion or awareness of deception or to admit to having prior knowledge about a study, though offering a reward and completing the PEI on a computer modestly improved awareness and admission rates. Study 4 indicated several reasons why participants may not reveal suspicion or knowledge about a study on the PEI.

Using traditional health capital model of Grossman (The human capital model of the demand for health. NBER, Working Paper 7078, 1972) and Wagstaff (Bull Econ Res 38(1):93–95,1986a) this paper attempts to fill in the theoretical missing link between mothers’ autonomy and household consumption behavior. We focus specifically on the consumption of child health inputs. In our paper it has been shown that working mothers’ children should be of better health. Further, independent of working status of the mother, higher autonomy always reallocates family budget to induce more consumption of child health inputs. The basic results of our model are further reinforced when autonomy is dependent on mothers’ income. In fact, the income effect derived from our extended model indicates that income-induced autonomy may result in redefining the composite consumption good for the family as an inferior one.