There has been an effort for a few decades to keep energy consumption at a minimum or at least within a low-level range. This effort is more meaningful and complex by including a customer’s satisfaction variable to ensure that customers can achieve the best quality of life that could be derived from how energy is used by different devices. We use the concept of Shapley Value from cooperative game theory to solve the multi-objective optimization problem (MOO) to responsibly fulfill user’s satisfaction by maximizing satisfaction while minimizing the power consumption, with energy constrains since highly limited resources scenarios are studied. The novel method uses the concept of a quantifiable user satisfaction, along the concepts of power satisfaction (PS) and energy satisfaction (ES). The model is being validated by representing a single house (with a small PV system) that is connected to the utility grid. The main objectives are to (i) present the innovative energy satisfaction model based on responsible wellbeing, (ii) demonstrate its implementation using a Shapley-value-based algorithm, and (iii) include the impact of a solar photovoltaic (PV) system in the energy satisfaction model. The proposed technique determines in which hours the energy should be allocated to maximize the ES for each scenario, and then it is compared to cases in which devices are usually operated. Through the proposed technique, the energy consumption was reduced 75% and the ES increased 40% under the energy constraints.

In order to achieve the objective of universal electricity access by 2030, new microgrids need to serve 630 million people globally. To achieve this goal, new finance models need to be developed and implemented. Load profiles of rural consumers are crucial for off-grid systems capacity planning and control. The combination of detailed load profiles with benefit assessment of rural electricity can provide important insights into the social dynamics of communities ready to adopt microgrid technologies and an important incentive to guide demand side management. We propose a Load Profile - Benefit Assessment (LPBA) methodology that integrates microgrid data, analyzed through mean shift clustering clustering technique, with mixed method instrumentation to adequately assess the impact electricity use has on consumers. Enabled by wellbeing assessment methodology, LPBA could change the perceived investment risk by taking a deeper look at the reasons why a rural electrification business succeeds or fails. Preliminary data from a case study of a rural secondary school in Tanzania is used here to illustrate the methodology. While microgrids connected to a reliable grid seek to reduce cost or maximize the hours of power output when the price per kWh is higher, the LPBA provides a general guideline for microgrid users to manage power consumption with the goal of maximizing wellbeing benefits. Initial results indicate that in order to scale up methodologies such as LPBA, innovation is needed in affordable smart meters, near-real time signal processing tools, and transactive energy appropriate technology.

In this study, we monitored power consumption to analyze the impact of energy usage on people's well-being. Data analytics is playing a key role in grid operation and the benefits derived from it. We use change point detection techniques to identify instances where power consumption time-series significantly change. Wavelets and adaptive chisquare and Ljung-Box tests are computed to detect these changes using real-world data. It has been shown that there is a strong positive correlation between quality of life and electricity consumption in impoverished countries. Once different levels of consumption are identified due to the use of different appliances, citizens well-being derived from these levels are linked to the relevant elements described in the Gallup-Healthways Report. Furthermore, it is important to come up with methodologies that rely on affordable meters, and thus allow users to manage their power consumption in a distributed manner and maximize their well-being.

Predicting mortality for ICU patients has become for many years a topic of interest within Health Institutions because ICUs represent large percentage of the budget spent on them. Several scoring systems have been developed to determine the probability of survival of a patient, which allows medical staff making timely decisions on their treatment and the correct distribution of the ICU resources. Alternately to existing scoring systems, this paper proposes a method that had not been used previously for the prediction of mortality, which is the Correntropy.