Bob Gill

AMPds: A public dataset for load disaggregation and eco-feedback research

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.

Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.

The cognitive power meter: Looking beyond the smart meter

The smart meter is often heralded as the key component supporting energy displays that can notify home occupants of their energy usage. But, a smart meter is only a digital power meter with enhanced communications capabilities - it is not actually smart. We need to look beyond the smart meter and define what intelligence is needed to actually make a meter smart. One area with promise is load disaggregation. Load disaggregation can be used to determine what loads contributing to the consumption reading at the smart meter. A smart meter incorporating load disaggregation intelligence can be seen as going beyond the traditional smart meter - what we call a cognitive power meter (c-meter). However, using load disaggregation, in its current form, is not feasible. We critically review the requirements for a c-meter and provide insights as to how load disaggregation research needs to change to make the c-meters a reality.

A Consumer Bill of Rights for Energy Conservation

Sustainable energy supply and demand can partially be solved by the conservation of energy, which is a personal and self-driven action. However, energy conservation currently requires the purchase of third-party products. The upfront cost of purchasing these products to monitor energy consumption in a home is a barrier that further cements the divide of those that have and those that have not. Detailed appliance power consumption reporting should be made available for free as part of the homes smart meter. Governments and power utilities must improve and expand policies that promote a socio-economic balance allowing everyone to participate in energy conservation regardless of their economic situation in a sustained way. We critically look at what economics and government polices exist and need to exist. We also demonstrate the computational means to achieve this - nonintrusive load monitoring (NILM) - and discuss how manufacturing and standards organizations need to work together to provide the essential information that describes how appliances consume energy. This paper proposes a Consumer Bill of Rights for Energy Conservation.

Inspiring energy conservation through open source power monitoring and in-home display

Many homeowners and occupants are interested in energy conservation for economical and/or ecological reasons. A number of commercial energy conservation solutions exist on the market today. However, these products contain closed systems and do not provide easy access to much of the raw data needed for more sophisticated analysis. An open source solution would be a great benefit for homeowners and occupants, allowing access to (and custom analysis of) raw power readings. We present a complete open source solution that monitors power, stores raw power readings, and makes provision for an in-home display, that informs stakeholders about energy consumption through a real-time ambient feedback effectively becoming an eco-feedback device.

Inspiring energy conservation through open source metering hardware and embedded real-time load disaggregation

Utility companies around the world are replacing electro-mechanical power meters with new smart meters. These digital power meters have enhanced communication capabilities, but they are not actually smart. We present the cognitive power meter (c-meter), a meter that is actually smart. By using load disaggregation intelligence, c-meter is the realization of demand response and other smart grid energy conservation initiatives. Our c-meter is made of two key components: a prototype open source ammeter and an optimized embedded load disaggregation algorithm (μDisagg). Additionally, we provide an open source multi-circuit ammeter array that can build probabilistic appliance (or load) consumption models that are used by the c-meter. μDisagg is the first load disaggregation algorithm to be implemented on an inexpensive low-power embedded processor that runs in real-time using a typical/basic smart meter measurement (current, in A). μDisagg can disaggregate loads with complex power states with a high degree of accuracy.

Bandwidth enhancement of microstrip patch antenna using Curve fitting based Differential Evolution and Cuckoo Search optimization - a comparative study

Microstrip patch antenna has extensive applications in areas requiring light weight and low profile antenna sub system. Differential Evolution (DE) algorithm and Cuckoo Search (CS) algorithm are modern optimization algorithms used here for determining the design parameters of microstrip antenna. In literature, there does not exist any direct relation between the bandwidth of a microstrip patch antenna with its design parameters. In this paper, a relationship between bandwidth and geometrical parameters of co-ax fed microstrip patch antenna has been established using Curve fitting model. The investigation is performed for microwave frequencies over a certain range. A comparative study is performed between the results obtained for bandwidth enhancement by optimizing the patch length, patch width and the position of feed using DE and CS algorithms.

Investigating vulnerabilities in GSM security

The telecommunications industry uses a combination of 2G (GSM), 3G (UMTS) and 4G (LTE) systems to access communication worldwide. Concurrent with a recent rise in cyber-attacks, the requirement for a secure infrastructure is increasingly crucial. However, while each new standard offers better protection to the mobile users, currently 84% of households in the UK have access to 3G signal from the four leading providers. Users are expected to be aware of the possible security threats, however auditing of the existing systems is responsibility of administrators. This paper highlights weaknesses and issues in the Global System for Mobile communications (GSM) standard, and presents an informed approach to help audit GSM networks for vulnerabilities.

The impact of big data on government processes

“Big data” is a term that is widely used by large corporations today. With access to a vast infrastructure and a large collection of personal information that is not publicly available, the United States of America (US) is able to complete researches and projects that are out of the reach of most countries and corporations. This paper will examine three US companies that have successfully utilized big data. US governments uses of big data across a variety of fields are examined as well. An evaluation will be concluded towards the end to analyze the advantages and disadvantages of US governments uses of big data comparing to those US companies that successfully utilized big data.

Elimination of higher order modes in wearable microstrip antenna

Reduction of higher order harmonic modes of the fundamental operating frequency in a wearable microstrip antenna has been proposed. Controlling harmonics in the microstrip antenna has been implemented by integrating simple I shaped resonator at the microstrip feed line. As a result higher order modes occurring between the fundamental resonating frequency and the second harmonics of the wearable microstrip antenna have been successfully suppressed.