Selected & Influential Publications

This page highlights a selection of my most influential publications — works that have contributed substantially to the understanding of building performance analysis, thermal/energy behaviour, building simulation, and climate‑related performance risks. These publications reflect long‑term research themes that run throughout my academic career: the integration of modelling and simulation into architectural design, the quantification of performance gaps, and the advancement of rigorous, evidence‑based approaches to building performance. A complete list of all publications is available on the Full Publication List page.

Building Performance Analysis (Wiley‑Blackwell, 2018)

Seminal research monograph — 640 pages, 1600+ references
This book provides a comprehensive and systematic overview of the field of building performance analysis. Widely used by PhD students, researchers, and advanced practitioners, it presents theoretical foundations, methodological frameworks, and practical applications for evaluating building performance across the entire lifecycle. Endorsed by IBPSA as only the second monograph ever recognised by the association.

Building Performance Basics (2022, 1st & 2nd editions)

A concise 80‑page primer on building performance
Aimed at BSc/MSc students and early‑career professionals, this book explains the “Why/What/Where/When/Who/How” of building performance in accessible language. It serves as a bridge between hands‑on tools (simulation, monitoring, surveys) and the broader systems thinking required of building performance champions in practice.

Energy Modelling (Chapter in the Handbook of Sustainable Building Design and Engineering, 2019)

Co‑authored with Godfried Augenbroe, this chapter provides a broad yet deep overview of building energy modelling, simulation tools, methodological challenges, and the role of modelling in sustainable building design.

PhD Thesis: Computational Support for the Selection of Energy Saving Building Components (2004)

Doctoral thesis exploring the role of building simulation in the design of energy‑efficient buildings, combining work at TU Delft, ECN and GeorgiaTech.

The gap between predicted and measured energy performance of buildings

de Wilde, P. (2014). Automation in Construction, 41, 40–49.
A landmark framework paper defining and clarifying the “energy performance gap.” Serves as a theoretical and methodological reference point for subsequent research on prediction accuracy, model calibration, and empirical validation.

Abstract: a persistent discrepancy exists between the expected (simulated) energy performance of buildings and the actual energy consumption recorded once they are in use. This article surveys the research on this so‑called “performance gap” and identifies three primary forms: (1) the gap between physics‑based simulation results and real measurements, (2) the gap between machine‑learning predictions and measurements, and (3) the gap between predicted performance and the values reported on regulatory energy certificates. The paper also introduces a pilot study that offers an initial probabilistic exploration of the performance gap. Insights from this study highlight several critical issues for future research, notably that the magnitude of the performance gap varies over time and depends on external conditions. The article concludes by arguing that reducing the performance gap requires a comprehensive and coordinated effort that integrates model validation and verification, improved data inputs for prediction models, more accurate forecasting methods, and changes in industry practice.

Intelligent computing for building performance analysis

de Wilde, P. (2018). In: Lecture Notes in Computer Science: Advanced Computing Strategies for Engineering.
This chapter examines the intersection of intelligent computing and building performance simulation, highlighting early integration of AI, optimisation, and computational reasoning in building analysis workflows.

Abstract: A key obstacle to making effective, intelligent use of computational methods in civil and architectural engineering lies in clearly defining the questions that information and communication technologies must answer. Some aspects of this challenge are implicitly addressed through activities such as establishing search and design spaces, creating model representations, formulating objective functions, developing ontologies, and designing multi‑criteria decision‑making approaches. Yet the fundamental motivations and requirements driving building design, construction, and facility management processes remain difficult to articulate—even though a clear understanding of them is crucial for meaningful application of computational tools. This paper examines the foundations of intelligent computing in the context of building performance assessment. It also investigates how methods from requirements engineering might contribute to more precise articulation of computational needs, while situating computational analysis within the broader set of assessment frameworks used in the building sector.

Management of thermal performance risks in buildings subject to climate change

de Wilde, P., & Tian, W. (2012). Building and Environment, 55, 167–177.
Explores probabilistic and risk‑based approaches to building thermal performance under future climate scenarios — a key contribution to climate‑robust building design.

Abstract: This paper examines how building performance simulation can be applied to evaluate the risks that climate change introduces for buildings’ thermal behaviour and for the essential services they are meant to deliver. Using several case studies, the article shows that forecasting a building’s likely thermal performance over the long time horizons associated with climate change involves substantial uncertainty. These same examples also demonstrate that interpreting the implications of projected changes is challenging, because the functions buildings are expected to fulfil often evolve over time. The paper concludes that while it is feasible to quantify climate‑related risks, such quantification comes with significant limitations. It also highlights the areas where further research is required to enable more productive discussions about acceptable risk levels and possible mitigation strategies for individual buildings.

Longitudinal prediction of the operational energy use of buildings

de Wilde, P., Tian, W., & Augenbroe, G. (2011). Building and Environment, 46(8), 1670–1680.
Develops longitudinal modelling approaches to predict operational energy use over time, accounting for degradation, uncertainty, and changing use conditions.

Abstract: To date, most investigations into buildings’ operational energy consumption do not adopt a long‑term perspective; in other words, they overlook how energy use evolves over the full service life of a building. Yet such a perspective is crucial for assessing climate‑change impacts or conducting long‑range energy accounting. This paper introduces a method for producing long‑term projections of operational energy demand. The approach is informed by an examination of how thermal performance deteriorates over time, how maintenance activities influence performance, and how future climate conditions may change. Central challenges include estimating the expected service life and thermal degradation of building components while simultaneously accounting for maintenance interventions and shifting weather patterns. Two examples are provided to demonstrate the use of deterministic and stochastic modelling approaches. The study concludes that long‑term prediction of operational energy use is achievable, but the accuracy of such forecasts depends heavily on extensive, high‑quality monitoring data—a requirement that most existing buildings do not currently satisfy.