Smart Urban Waste Heat Recovery via IoT-Driven District Cooling Networks

Thesis Abstract

Urban energy systems face pressures from rising demand, decarbonization targets, and the variability of renewable supply, making efficient heat management essential. This study addresses the underutilization of waste heat in dense urban environments by designing and evaluating an IoT-driven district cooling network (DCN) that captures, stores, and redistributes waste heat from commercial, residential, and industrial sources to serve cooling loads across a multi-tenant urban district. The aim is to quantify the energy, economic, and environmental benefits of integrating IoT-enabled heat recovery with district cooling, and to identify governance and operational conditions that maximize system performance. Specific objectives are to (1) characterize heat streams, temporal availability, and spatial distribution of urban waste heat; (2) develop an ICT architecture incorporating sensor networks, real-time data fusion, predictive analytics, and automated control for a modular DCN; (3) evaluate techno-economic performance through a pilot implementation in a 1.2 km2 urban precinct with 25 buildings and an estimated peak cooling demand of 60 MW; (4) analyze system resilience under demand shocks and climate variability using scenario analysis; and (5) formulate a governance framework addressing data privacy, tariff structures, and interoperability standards to enable scalable replication. The methodology adopts a mixed-methods research design combining an engineering feasibility study with empirical performance evaluation. The population comprises three components urban heat sources (buildings and infrastructure with potential waste heat), the DCN pilot site, and end-user stakeholders (building managers, city planners, and utility operators). A target sample of 40 heat-source nodes and 25 end-user buildings will be instrumented for one year to capture diurnal and seasonal variations. Data collection instruments include IoT sensors for temperature, flow, and heat flux, sub-metering for cooling consumption, SCADA for network operations, and structured interviews with stakeholders. Data analysis employs a two-tier approach (i) technical performance analysis using regression models to relate waste-heat availability to cooling load reductions, time-series forecasting with ARIMA and gradient boosting for demand prediction, and reliability assessment through fault-tree and Monte Carlo simulation; (ii) economic and environmental impact assessment using net present value (NPV), levelized cost of cooling (LCOC), internal rate of return (IRR), and life-cycle greenhouse gas (GHG) emissions analysis following IPCC accounting methods. The conceptual framework integrates the Resource-Based View (RBV) for capability building in urban ICT-enabled energy management and the Diffusion of Innovations (DOI) theory to explain adoption dynamics, complemented by the Physical Principles of heat exchange and the Internet of Things architecture layers (sensing, communication, and control). Expected findings indicate that IoT-enabled DCN can achieve 25–40% annual energy savings in cooling energy, with peak-load reductions >50% during extreme heat events; dispatch algorithms will reduce pumping energy by 15–20% and enhance system resilience against outages by enabling rapid reconfiguration. Economic analysis anticipates a positive NPV within seven years under conservative tariff assumptions, with IRR aligned to existing utility benchmarks, and GHG emissions reductions in the 15–25% range depending on refrigerant choice and electricity mix. Sensitivity analyses will reveal critical parameters such as heat source reliability, data quality, and consumer acceptance, while scenario analyses will illustrate performance under climate warming and urban densification. The study contributes to knowledge by operationalizing a scalable, ICT-driven framework for urban waste-heat recovery integrated with district cooling, bridging theoretical constructs from RBV and DOI with practical engineering design and policy considerations. It offers a replicable architectural blueprint for data governance, interoperability standards, and market mechanisms that incentivize waste-heat capture. The main conclusion is that IoT-enabled DCNs can transform urban cooling, yielding substantive energy and emissions benefits while delivering economic viability within a municipal governance context. Recommendations include standardization of sensor protocols, data-sharing agreements, tariff models to reflect heat-energy externalities, and phased rollouts in other high-density urban areas, accompanied by long-term monitoring to refine predictive controls and sustain performance gains.