Economic parameters in the evaluation studies focusing on building energy efficiency : a review of the underlying rationale, data sources, and assumptions

A growing literature has highlighted the variables and parameters that most affect the technical feasibility and the economic viability of the measures meant to improve building energy efficiency. This paper discusses the results of a literature review, which focuses on the studies that deal with three economic parameters: the price to be paid for the energy supply, the energy inflation rate, and the discount rate used to convert future cash flows to a present value, namely, an upfront lump-sum equivalent. A specific co-occurrence analysis of terms is performed on the titles and abstracts of the examined documents. The representation of the results allows recognizing several significant clusters and network relationships. Moreover, that literature review enables to identify two well-established research strands. The first involves the relationship between energy prices and the profitability of efficiency-related investments. The second research branch points at the pivotal role played by the discount rate when evaluating the investments in energy-efficient measures

Advanced Statistical Signal Processing Methods in Sensing, Detection, and Estimation for Communication Applications

The applications of wireless communications and digital signal processing have dramatically changed the way we live, work, and learn over decades. The requirement of higher throughput and ubiquitous connectivity for wireless communication systems has become prevalent nowadays. Signal sensing, detection and estimation have been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal sensing, detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal sensing, detection and estimation schemes for wireless communications applications, such as spectrum sensing, symbol-detection/channel-estimation, and encoder identification. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Robust optimization for home-load scheduling under price uncertainty in smart grids

In this paper, the emerging problem of residential energy scheduling for smart grid under the energy-price uncertainty is investigated, where the prices randomly vary around nominal values with a known underlying distribution. The objective of the focused optimization problem is to minimize the user\u27s cost of energy consumption. The robust optimization methodology will be used to deal with the uncertainty programming. We schedule the amounts of charged/discharged energy of the energy storage devices varied continuously within each time slot, which is more realistic and cost-effective. In addition, the mathematical model we adopts here is more practical because we consider the linear dynamical model of the temperature-related appliances. The involvement of the price uncertainty makes our devised algorithm more robust. Simulation results illustrate that our proposed new scheme is more effective than the conventional method without smart grid in terms of total energy cost

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS.

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS