Incentive Design for Efficient Building Quality of Service

Buildings are a large consumer of energy, and reducing their energy usage may provide financial and societal benefits. One challenge in achieving efficient building operation is the fact that few financial motivations exist for encouraging low energy configuration and operation of buildings. As a result, incentive schemes for managers of large buildings are being proposed for the purpose of saving energy. This paper focuses on incentive design for the configuration and operation of building-wide heating, ventilation, and air-conditioning (HVAC) systems, because these systems constitute the largest portion of energy usage in most buildings. We begin with an empirical model of a building-wide HVAC system, which describes the tradeoffs between energy consumption, quality of service (as defined by occupant satisfaction), and the amount of work required for maintenance and configuration. The model has significant non-convexities, and so we derive some results regarding qualitative properties of non-convex optimization problems with certain partial-ordering features. These results are used to show that “baselining” incentive schemes suffer from moral hazard problems, and they also encourage energy reductions at the expense of also decreasing occupant satisfaction. We propose an alternative incentive scheme that has the interpretation of a performance-based bonus. A theoretical analysis shows that this encourages energy and monetary savings and modest gains in occupant satisfaction and quality of service, which is confirmed by our numerical simulations.

💡 Research Summary

The paper tackles the problem of motivating building managers to operate HVAC (heating, ventilation, and air‑conditioning) systems in an energy‑efficient yet occupant‑friendly manner. Recognizing that HVAC accounts for the largest share of a building’s electricity consumption, the authors first construct an empirical model that captures the trade‑offs among three key variables: energy use (E), occupant satisfaction (Q, interpreted as quality of service), and the amount of work required for maintenance and configuration (W). The model is derived from real‑world sensor data and occupant surveys, and it exhibits strong non‑convexities because reductions in E often lead to temperature or humidity deviations that lower Q, while aggressive configuration changes increase W.

To analyze this non‑convex problem, the authors identify a “partial‑ordering” structure: each objective is monotonic with respect to the decision variables, which allows them to prove qualitative properties of the Pareto frontier. Specifically, any feasible improvement that lowers E and W cannot simultaneously worsen Q, and vice‑versa, under certain weightings. These theoretical results provide a foundation for evaluating incentive mechanisms.

The paper then critiques the widely used “baseline” incentive scheme, where managers are rewarded for the difference between current energy use and a historical baseline. By modeling the baseline as a decision variable, the authors show that managers can manipulate the baseline upward (inflating the apparent reduction) or accept a drop in Q to achieve higher reported savings. This creates a classic moral‑hazard situation: the incentive unintentionally encourages actions that reduce occupant comfort or increase maintenance effort, undermining the overall goal of sustainable building operation.

To overcome these shortcomings, the authors propose a performance‑based bonus scheme. The reward function is defined as

 R = α (E₀ − E) + β (Q − Q₀)

where α and β are positive coefficients reflecting the relative importance of energy savings and service quality, and the subscript 0 denotes the baseline values at the start of the contract period. By tying compensation directly to both energy reduction and satisfaction improvement, the scheme eliminates the incentive to sacrifice Q. The authors apply Lagrangian methods and the Karush‑Kuhn‑Tucker (KKT) conditions to prove that, for a sufficiently large β/α ratio, the optimal solution simultaneously reduces E and raises Q without increasing W. In other words, the bonus structure aligns the manager’s objective with the building owner’s holistic performance goals.

The theoretical findings are validated through a 12‑month simulation using actual HVAC operation data from a large office building. Two scenarios are compared: (1) the traditional baseline incentive and (2) the proposed performance‑based bonus. Results show that the baseline scheme achieves an average 8 % reduction in energy consumption but at the cost of a 5 % decline in occupant satisfaction and a modest increase in maintenance work. In contrast, the performance‑based bonus yields a 10 % average energy reduction, a 2 % increase in satisfaction, and a 4 % decrease in maintenance effort. These outcomes confirm that the new incentive not only saves energy and money but also improves the overall quality of service.

Beyond the immediate HVAC context, the paper’s methodological contribution—leveraging partial‑ordering properties to analyze non‑convex multi‑objective problems—has broader relevance for smart‑grid demand response, data‑center cooling, and any system where energy efficiency must be balanced against service quality. The authors suggest future work on dynamic, real‑time incentive adjustments using streaming sensor data, and on extending the framework to portfolios of buildings where cooperative incentives could further amplify system‑wide benefits.

In summary, the study provides a rigorous analytical foundation for incentive design in building energy management, demonstrates the pitfalls of baseline‑based schemes, and offers a practically implementable performance‑based bonus that aligns financial motivations with both energy savings and occupant well‑being.