Pathways to Net-Zero Energy Buildings: An Optimization Methodology

Building Performance Simulation (BPS) is frequently used by decision-makers to estimate building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error methods of building simulation are used to identify combinations of parameters to reduce energy use. Optimization techniques combined with BPS offer many benefits such as: (i) identification of potential optimal designs which best achieve desired performance objectives; (ii) system level component integration by simultaneously considering conflicting trade-offs; and (iii) a process-oriented simulation tool that is complementary to BPS, eliminating the need for repetitive userinitiated model evaluations. However, the capability of optimization algorithms to effectively map out the entire solution space and discover information is farther reaching than building design. As shown in this thesis, optimization datasets are also a valuable resource for conducting uncertainty and sensitivity analyses and evaluating policies to incentivize low-energy building design. Two performance criteria are considered in this thesis: net-energy consumption and life-cycle cost. The term ‘performance-optimized’ refers to the extreme of these two criteria that is Net-Zero Energy (NZE) and cost-optimized buildings. A Net-Zero Energy Building (NZEB) generates at least as much renewable energy on-site as it consumes in a given year. A cost-optimized building has the lowest life-cycle cost over a considered period. A focus of this thesis is identifying optimal pathways to NZE and cost-optimized building designs. This thesis proposes the following approaches to identify pathways to net-zero energy: (i) a redesign case-study of an existing near-Net-Zero Energy Home (NZEH) archetype using a proposed optimization methodology; (ii) the development of an information-driven hybrid evolutionary algorithm for optimal building design; (iii) a methodology for identifying the influence of design variations on building energy performance; (iv) a methodology to evaluate the effect of incentives on life-cycle energy-cost curves; and (v) effect of a time-of-use feed-in tariff on optimal net-zero energy home design. The optimization methodology consists of: (i) an energy model; (ii) a cost model; (iii) a custom optimization algorithm; (iv) a database; and (v) a statistics module. Several new simulation techniques are proposed to identify pathways to performanceoptimized net-zero energy buildings: (i) probability distribution functions extracted from previous simulations; (ii) back-tracking searches; and (iii) importance factors to summarize back-tracking search results. This thesis provides valuable information related to: (i) the development of performancebased energy codes for buildings; (ii) systematic design of cost-optimized NZEHs; (iii) systematic analysis of the impact of different design parameters on energy consumption and cost; (iv) the study of incentive measures for NZEHs

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

Optimal seismic retrofitting of existing RC frames through soft-computing approaches

2016 - 2017Ph.D. Thesis proposes a Soft-Computing approach capable of supporting the engineer judgement in the selection and design of the cheapest solution for seismic retrofitting of existing RC framed structure. Chapter 1 points out the need for strengthening the existing buildings as one of the main way of decreasing economic and life losses as direct consequences of earthquake disasters. Moreover, it proposes a wide, but not-exhaustive, list of the most frequently observed deficiencies contributing to the vulnerability of concrete buildings. Chapter 2 collects the state of practice on seismic analysis methods for the assessment the safety of the existing buildings within the framework of a performancebased design. The most common approaches for modeling the material plasticity in the frame non-linear analysis are also reviewed. Chapter 3 presents a wide state of practice on the retrofitting strategies, intended as preventive measures aimed at mitigating the effect of a future earthquake by a) decreasing the seismic hazard demands; b) improving the dynamic characteristics supplied to the existing building. The chapter presents also a list of retrofitting systems, intended as technical interventions commonly classified into local intervention (also known “member-level” techniques) and global intervention (also called “structure-level” techniques) that might be used in synergistic combination to achieve the adopted strategy. In particular, the available approaches and the common criteria, respectively for selecting an optimum retrofit strategy and an optimal system are discussed. Chapter 4 highlights the usefulness of the Soft-Computing methods as efficient tools for providing “objective” answer in reasonable time for complex situation governed by approximation and imprecision. In particular, Chapter 4 collects the applications found in the scientific literature for Fuzzy Logic, Artificial Neural Network and Evolutionary Computing in the fields of structural and earthquake engineering with a taxonomic classification of the problems in modeling, simulation and optimization. Chapter 5 “translates” the search for the cheapest retrofitting system into a constrained optimization problem. To this end, the chapter includes a formulation of a novel procedure that assembles a numerical model for seismic assessment of framed structures within a Soft-Computing-driven optimization algorithm capable to minimize the objective function defined as the total initial cost of intervention. The main components required to assemble the procedure are described in the chapter: the optimization algorithm (Genetic Algorithm); the simulation framework (OpenSees); and the software environment (Matlab). Chapter 6 describes step-by-step the flow-chart of the proposed procedure and it focuses on the main implementation aspects and working details, ranging from a clever initialization of the population of candidate solutions up to a proposal of tuning procedure for the genetic parameters. Chapter 7 discusses numerical examples, where the Soft-Computing procedure is applied to the model of multi-storey RC frames obtained through simulated design. A total of fifteen “scenarios” are studied in order to assess its “robustness” to changes in input data. Finally, Chapter 8, on the base of the outcomes observed, summarizes the capabilities of the proposed procedure, yet highlighting its “limitations” at the current state of development. Some possible modifications are discussed to enhance its efficiency and completeness. [edited by author]XVI n.s

Hybridization and discretization techniques to speed up genetic algorithm and solve GENOPT problems

One of the challenges in global optimization is to use heuristic techniques to improve the behaviour of the algorithms on a wide spectrum of problems. With the aim of reducing the probabilistic component and performing a broader and orderly search in the feasible domain, this paper presents how discretization techniques can enhance significantly the behaviour of a genetic algorithm (GA). Moreover, hybridizing GA with local searches has shown how the convergence toward better values of the objective function can be improved. The resulting algorithm performance has been evaluated during the Generalization-based Contest in Global Optimization (GENOPT 2017), on a test suite of 1800 multidimensional problems

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

A Study of Predictive Control Strategies for Optimally Designed Solar Homes

This thesis investigates the development of predictive control strategies for optimally or near-optimally designed solar homes. Optimal design refers to the integration of renewable energy technologies (mainly active and passive solar) with a high-quality building envelope as well as efficiency and conservation measures to achieve substantial reductions in energy consumption and peak demand. Effective implementation of these technologies requires an integrated design approach, which considers their interactions with the building and its services. Furthermore, control strategies must be an essential part of the integrated design of a building to improve energy performance and ensure occupant comfort. In optimally designed solar homes, control strategies should incorporate the collection, storage and delivery of solar energy. Weather forecasts along with an understanding of the building’s thermal dynamics (e.g., time delays due to thermal mass) enable predicting and managing loads and solar energy availability. Design and operation strategies of a case study, the Alstonvale House, are presented. Features of this house include passive solar design, a building-integrated photovoltaic/thermal (BIPV/T) system coupled with a solar-assisted heat pump, a thermal energy storage tank and a radiant floor heating system in a thermally massive concrete slab. Design and control approaches developed for the Alstonvale House provided the basis for generalized control strategies applicable to optimally designed solar homes. Simplified building models, which can be derived from more detailed models or on-site measurements, can facilitate the implementation of predictive control techniques. In this investigation, model-based predictive control was applied to a radiant floor heating system and the position of roller blinds in a room with high solar gains. Predictive control can also be applied to optimize the operation of renewable energy systems. In this study, forecasts of heating loads and solar radiation were used in a dynamic programming algorithm to select a near-optimal set-point trajectory for an energy storage tank heated with a heat pump assisted by a BIPV/T system

ECOS 2012

The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology