Christian Ghiaus

Steady-state thermal–hydraulic network models are widely used for the analysis, design, and operation of energy systems. While direct problems with prescribed boundary conditions can often be solved efficiently, inverse problems such as set-point tracking and parameter identification are commonly addressed through repeated solution of the corresponding direct problem. For large-scale networks with strong nonlinear couplings, such nested strategies can become computationally expensive and numerically burdensome. This paper presents a unified methodology for the solution of direct and inverse steady-state thermal–hydraulic problems within a single modeling workflow. In contrast to classical nested approaches, inverse problems are formulated in a simultaneous analysis and design framework, in which system states and selected system inputs are treated as unknowns simultaneously. The methodology combines externally causal component representations with acausal network balance relations in order to expose the structural dependencies of the assembled system and enable graph-based tearing reduction. Component-local evaluations, including possible component-internal nonlinear calculations, are encapsulated within the component models, while the nonlinear network closure problem is restricted to a reduced set of tearing variables.. Direct problems are solved by nonlinear root finding on the tearing-reduced residual system, whereas inverse problems are posed as tearing-reduced residual-constrained nonlinear programs with equality, inequality, and bound constraints. The methodology is demonstrated on a vapor-compression refrigeration cycle, where compressor speed and expansion valve opening are adjusted to satisfy prescribed cooling-load and superheat targets under varying condenser inlet temperatures. Implemented in Python, the proposed methodology supports transparent and reproducible modeling and provides a practical basis for simulation, set-point tracking, and constrained optimization of coupled thermal–hydraulic networks.

Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

This paper proposes the method pELECTRE Tri, a novel probabilistic Multi-Criteria Decision Making (MCDM) method using the traditional ELECTRE Tri, probability distribution, and Monte Carlo simulation to support informed decision-making in complex and uncertain decision environments. The proposed method is illustrated through a case study involving the renovation of three social housing buildings. The paper provides new insights into the application of probabilistic sorting MCDM in the context of energy efficiency in buildings and highlights the benefits of using probabilities rather than crisp values to categorize alternatives enabling stakeholders to make better use of available resources, especially when dealing with a large dataset of energy measures with different features. The methodology implemented in Python (DOI: 10.5281/zenodo.7967655) is available as an open source.

Decision-making in energy retrofit of existing buildings needs to consider the economic and technical criteria, as well as social and environmental ones. The main problems are to define and to aggregate these contradicting issues to support a measurable and verifiable decision process. The working hypothesis of this paper is that the ELECTRE Tri method would respond to the problem. The paper proposes and tests in a real professional environment the ELECTRE Tri method to help choose a retrofit program for collective residential buildings. The evaluation criteria used as performance indicators are defined in consultation with the decision maker and weighted by using the revised Simos’ cards method. The paper proposes to construct the renovation scenarios by combining individual actions that are mutually compatible. The methodology, the proposed criteria and the scenarios were successfully applied on a renovation project of a residential assembly composed of three buildings (67 apartments). A new solution to the problem of defining reference scenarios specific to the ELECTRE Tri method is also proposed. The main advantages of the proposed methodology are the formalization of the decision-making process within the framework of energy renovation strategies and the application of a robust multi-criteria analysis method, considering the complexity of the problem. The main drawbacks are the cost (in time and resources) for obtaining the information needed to construct the decision matrix and the precision of the weights of the criteria. The methodology implemented in Python is available in open source.

Psychrometric chart is a basic tool for analysis of processes in air-conditioning systems. While psychrometric calculators and numerical psychrometric charts are widely available, there is a lack of numerical algorithms for solving the sizing problem by using psychrometric analysis. After introducing a classification of modelling problems in direct and inverse (based on the relation between physical and computational causality), this paper defines the design problem as a set of inverse problems of control and parameter optimization. A non-linear model is obtained by assembling the models of the elementary processes. The paper proposes to solve the direct and control problems by using a method similar to Newton-Raphson’s, and the parameter optimization problem by using least-squares. Open-source implementation, published on Zenodo repository, and interactive, reproducible environment, available on Binder web service, accompany this paper (Ghiaus [2021]. PsychroAn_cool: Psychrometric analysis of cooling systems as a control problem. Zenodo.).