Adam Zipperer

Bio: Adam Zipperer is an academic researcher from Colorado State University. The author has contributed to research in topics: Smart grid & Electric power industry. The author has an hindex of 3, co-authored 3 publications receiving 127 citations.

Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

Abstract: Smart homes hold the potential for increasing energy efficiency, decreasing costs of energy use, decreasing the carbon footprint by including renewable resources, and transforming the role of the occupant. At the crux of the smart home is an efficient electric energy management system that is enabled by emerging technologies in the electricity grid and consumer electronics. This paper presents a discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage.

On the design of a survey for reconciling consumer behaviors with demand response in the smart home

Abstract: There are many avenues of current and future research for addressing peak load strain on the U.S. electricity grid. With peak loads in many areas mainly comprising residential loads, the opportunities for residential demand response are great. Behavior changes accompanying technical solutions hold the promise of large and long lasting energy savings and peak reductions. By engaging disciplines outside the typical domain of the electric power industry, specifically psychology, there is the opportunity to motivate residential customers. This may be achieved by customized prompts and feedback designed to change behaviors. The result may lead to more efficient operation of the distribution grid, substantial peak load reductions, and efficiency gains. To that end, this paper presents two potential multi-criteria decision-making methodologies as they would apply to a residential building energy management system. The control system would need user-specific input, and this paper presents a procedure that incorporates fundamentals of social psychology for developing a survey to ascertain user preferences.

An application of a decision-making algorithm for an intelligent distribution substation

Abstract: In this paper, the expected functionality and the operational priority of an intelligent distribution MV/LV substation is evaluated. The analytic hierarchy process is applied as a multi-criteria decision-making tool for evaluation based on a survey of expectations among expert responders. The results are presented in terms of the priority ranked criteria as well as the available alternatives, such that the alternatives are ranked based on their fulfillment of the expected criteria and functionality.

Electricity market design for the prosumer era

Abstract: Prosumers are agents that both consume and produce energy. With the growth in small and medium-sized agents using solar photovoltaic panels, smart meters, vehicle-to-grid electric automobiles, home batteries and other ‘smart’ devices, prosuming offers the potential for consumers and vehicle owners to re-evaluate their energy practices. As the number of prosumers increases, the electric utility sector of today is likely to undergo significant changes over the coming decades, offering possibilities for greening of the system, but also bringing many unknowns and risks that need to be identified and managed. To develop strategies for the future, policymakers and planners need knowledge of how prosumers could be integrated effectively and efficiently into competitive electricity markets. Here we identify and discuss three promising potential prosumer markets related to prosumer grid integration, peer-to-peer models and prosumer community groups. We also caution against optimism by laying out a series of caveats and complexities.

Socio-technical evolution of Decentralized Energy Systems: A critical review and implications for urban planning and policy

Abstract: The growth of Decentralized Energy Systems (DES) signals a new frontier in urban energy planning and design of local energy systems. As affordability of renewable energy technologies (RET) increases, cities and urban regions become the venues, not only for energy consumption but also for generation and distribution, which calls for systemic and paradigmatic change in local energy infrastructure. The decentralizing transitions of urban energy systems, particularly solar photovoltaic and thermal technologies, require a comprehensive assessment of their sociotechnical co-evolution – how technologies and social responses evolve together and how their co-evolution affects urban planning and energy policies. So far, urban planning literature has mainly focused on the impact of physical urban forms on efficiency of energy consumption, overlooking how the dynamics of new energy technologies and associated social responses affect local systems of energy infrastructure, the built environments and their residents. This paper provides an interdisciplinary review on the co-evolving technical and social dynamics of DES focusing on Distributed Generation (DG), MicroGrids (MG), and Smart MicroGrids (SMG), in order to draw insights for their integration in urban planning and policy, in particular reference to climate change mitigation and adaptation planning.

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Abstract: Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.