Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Work, consumption and subjectivity in postwar France: Moulinex and the meanings of domestic appliances, 1950s-1970s

This article responds to some of the limitations of the historiography of consumption in contemporary Europe, notably its tendency to divorce consumer culture from production and to subscribe, in some cases at least, to a rather schematic model of ‘consumer society’. Focusing on the Moulinex domestic appliance company which developed in Normandy from the late 1950s, it explores the interpenetration of cultures of production at several levels. It considers the role of Moulinex in making domestic appliances available to the mass market, the place of productivism in the Moulinex brand and the place of appliance consumption in company culture, before reflecting on the workers’ perspective on this culture and the meanings they ascribed to the appliances they acquired through the company

Optimal provision of distributed reserves under dynamic energy service preferences

We propose and solve a stochastic dynamic programming (DP) problem addressing the optimal provision of regulation service reserves (RSR) by controlling dynamic demand preferences in smart buildings. A major contribution over past dynamic pricing work is that we pioneer the relaxation of static, uniformly distributed utility of demand. In this paper we model explicitly the dynamics of energy service preferences leading to a non-uniform and time varying probability distribution of demand utility. More explicitly, we model active and idle duty cycle appliances in a smart building as a closed queuing system with price-controlled arrival rates into the active appliance queue. Focusing on cooling appliances, we model the utility associated with the transition from idle to active as a non-uniform time varying function. We (i) derive an analytic characterization of the optimal policy and the differential cost function, and (ii) prove optimal policy monotonicity and value function convexity. These properties enable us to propose and implement a smart assisted value iteration (AVI) algorithm and an approximate DP (ADP) that exploits related functional approximations. Numerical results demonstrate the validity of the solution techniques and the computational advantage of the proposed ADP on realistic, large-state-space problems

Responsible Environmental Behavior, Energy Conservation, and Compact Fluorescent Bulbs: You Can Lead a Horse to Water, But Can You Make It Drink?

Despite professing to care about the environment and supporting environmental causes, individuals behave in environmentally irresponsible ways like driving when they can take public transportation, littering, or disposing of toxic materials in unsound ways. This is the author\u27s fourth exploration of how to encourage individuals to stop behaving irresponsibly about the environment they allege to care deeply about. The prior three articles all explored how the norm of environmental protection could be enlisted in this effort; this article applies those theoretical conclusions to the very practical task of getting people to switch the type of light bulb they use. To accomplish this, the article synthesizes the previous articles into an assumption about the critical role of norms in changing personal behavior and tests that assumption by exploring how to make individuals more responsible consumers of electricity and adhere to the concrete norm of energy conservation by swapping out their incandescent light bulbs for compact fluorescent lights (“CFLs”). The agreed upon goal behind energy conservation is to reduce the country’s reliance on fossil fuel-based energy production, thus reducing the emission of harmful airborne pollutants and greenhouse gases as well as the related environmental harms associated with coal production. One way to reduce residential energy consumption is to persuade individuals to switch to CFLs. Up to ninety percent of energy produced by incandescent bulbs is lost as heat; switching to CFLs is one way to prevent this energy loss

Hierarchical and Distributed Architecture for Large-Scale Residential Demand Response Management

The implementation of smart grid brings several challenges to the power system. The ‘prosumer’ concept, proposed by the smart grid, allows small-scale ‘nano-grids’ to buy or sell electric power at their own discretion. One major problem in integrating prosumers is that they tend to follow the same pattern of generation and consumption, which is un-optimal for grid operations. One tool to optimize grid operations is demand response (DR). DR attempts to optimize by altering the power consumption patterns. DR is an integrated tool of the smart grid. FERC Order No. 2222 caters for distributed energy resources, including demand response resources, in participating in energy markets. However, DR contribution of an average residential energy consumer is insignificant. Most residential energy consumers pay a flat price for their energy usage and the established market for residential DR is quite small. In this dissertation, a survey is carried out on the current state-of-the-art in DR research and generalizations of the mathematical models are made. Additionally, a service provider model is developed along with an incentive program and user interfaces (UI). These UIs and incentive program are designed to be attractive and easily comprehended by a large customer base. Furthermore, customer behavior models are developed that characterize the potential customer base, allowing a demand response aggregator to understand and quantify the quality of the customer. Optimization methods for DR management with various characteristics are also explored in this dissertation. Moreover, A scalable demand response management framework that can incorporate millions of participants in the program is introduced. The framework is based on a hierarchical architecture. To improve DR management, hierarchical load forecasting method is studied. Specifically, optimal combination method for hierarchical forecast reconciliation is applied to the DR program. It is shown that the optimal combination for reconciliation of hierarchical predictions could reduce the stress levels of the consumer close to the ideal values for all scenarios

A RESIDENTIAL ELECTRICAL LOAD MODEL

This work develops a time varying residential electric load model based on the philosophy that the availability of people to turn on electrical appliances and their tendency to do so at particular times are significant factors in determining the time varying nature of the residential load. Additional contributions to the load are the result of peoples availability and tendency to perform direct actions (e.g. dish washing) that indirectly cause electrical appliances (e.g. water heaters) to turn on and off under the control of their sensors. The availability and tendencies of the residents also affect, to some extent, the contribution to the load caused by weather conditions since the residents determine the settings of heating and cooling devices. A general availability function is developed to estimate the number of persons available in the residence at a particular time. Proclivity (tendency) functions are developed to specify the probability that an appliance will be used at a particular time by an available person or that the person will perform an action which will result in the operation of some appliance. Models for individual appliances, selected on the basis of their load significance, are developed using the foregoing functions together with operating characteristics of the appliances and estimates of power consumption for prevalent sizes. The individual appliance models are combined into a residential model with provisions for specifying characteristics of the residence. The model is used to simulate individual residences and groups of residences. Heating and cooling loads are not included in the simulation. The load curves generated by the simulation are compared to test data obtained during the Connecticut Light and Power Company Residential Load Test {1} for equivalent residences and groups of residences. The results indicate that the model has potential for estimating the time varying residential electric load

A Balancing Demand Response Clustering Approach of Domestic Electricity for Day-Ahead Markets

This paper introduces a new clustering approach for multi-customer intelligent demand response for customers living in the same or closer smart grid locations using real electricity consumption data from smart meters. Most of the demand side management or customer tariffs focused on a single customer to optimize their usage discarding the others connected to the same grid. The proposed balancing clustering focus on the customers connected to the same or closest grid to optimize the smooth operating of the energy producers. This approach offers a triple win-win-win model for peak and low consumption customers as well as the balancing for the producer/ distributor utility companies for planning the day ahead markets. This paper uses the most widely used clustering method of k-means for finding similar customers on the opposing side peak, low consumption profiles and combines the most distinguished customers forming more uniform consumption for day-ahead market. This customer balancing and grouping them provides a better way toaggregate residential load data for power buy and sell for all sides and results in better load scheduling

Examining the customer journey of solar home system users in Rwanda and forecasting their future electricity demand

Globally, 771 million people lack access to electricity, out of which 75% live in Sub-Saharan Africa (IEA, 2020b). Electricity grid expansion can be costly in rural areas, which often have low population densities. Solar home systems (SHS) have provided people worldwide an alternative option to gain electricity access. A SHS consists of a solar panel, battery and accompanying appliances. This research aims to advance the understanding of the SHS customer journey using a case study of SHS customers in Rwanda. This study developed a framework outlining households’ pre- to post-purchase experiences, which included awareness and purchase, both current and future SHS usage and finally customers’ upgrade, switching and retention preferences. A mixed methods approach was utilised to examine these steps, including structured interviews with the SHS providers’ customers (n=100) and staff (n=19), two focus groups with customers (n=24), as well as a time series analysis and descriptive statistics of database customers (n=63,299). A convolutional neural network (CNN) was created to forecast individual SHS users’ future electricity consumption in the next week, month and three months based on their previous hourly usage. Despite the volatility of SHS usage data, the CNN was able to forecast individual users’ future electricity more accurately than the naïve baseline, which assumes a continuation of previous usage. The time series analysis revealed an evening usage peak for non-television users, whilst customers with a television experienced an additional peak around midday. SHS recommendations prior and post-purchase were common, highlighting the circular nature of the customer journey. The main purchase reason and usage activity were having a clean energy source and phone charging respectively. A better understanding of the SHS customer journey may increase the number of households with electricity access, as companies can better address the purchase barriers and tap into the power of customer recommendations