Decarbonization in buildings calls for advanced control strategies that coordinate on-site renewables, grid electricity, and thermal demand. Literature approaches typically rely on demand side management strategies or on active energy storage, like batteries. However, the first solution often neglects carbon-aware objectives, and could lead to grid overload issues, while batteries entail environmental, end-of-life, and cost concerns. To overcome these limitations, we propose an optimal, carbon-aware optimization strategy that exploits the building's thermal mass as a passive storage, avoiding dedicated batteries. Specifically, when a surplus of renewable energy is available, our strategy computes the optimal share of surplus to store by temporarily adjusting the indoor temperature setpoint within comfort bounds. Thus, by explicitly accounting for forecasts of building energy consumption, solar production, and time-varying grid carbon intensity, our strategy enables emissions-aware load shifting while maintaining comfort. We evaluate the approach by simulating three TRNSYS models of the same system with different thermal mass. In all cases, the results show consistent reductions in grid electricity consumption with respect to a baseline that does not leverage surplus renewable generation. These findings highlight the potential of thermal-mass-based control for building decarbonization.
Figures & tables
Figure 1: CO2 Emissions from the building sector from 2010 to 2023 (left) and the share of buildings in global energy- and process-related emissions in 2023 (right). Source: reproduced from [ 44 ] .
Figure 2: Flowchart illustrating the stepwise development, implementation, and application of the proposed optimization strategy.
Building
Classification
Configuration
Stratigraphy
Thickness
Thermal
Surface
Attenuation
Phase
Internal Areal Heat
Thermal
Envelope
Subfigure
[ cm ]
Trans. [ W/m2K ]
Mass [ kg/m2 ]
Shift [h]
Capacity [kJ/m2K]
Capacity [kJ/K]
LIGHT-WEIGHT
External
3130.83
opaque
Wall
External
(a)
20.9
0.206
49
0.608
5.39
24.3
enclosure
Internal
Table 1: Building envelope characteristics. This table reports the main properties of the building envelope based on the stratigraphy adopted for the design of the building hosting the considered office room, for each configuration analyzed in the case study. The stratigraphy labels are referred to Figures 11 , 12 , 13 of A .
Parameter
Type
Value
Internal gain
Artificial lighting
6W/m2 , active during working hours (09:00–19:00)
Occupancy
130W per person (ASHRAE standard for moderately
active office work [29]). During working days (09:00–19:00),
the number of occupants varies between 0, 2, or 4; weekends: 0.
Control and operation
Hourly air change rate
0.6ACH
(infiltration and natural ventilation)
Table 2: Boundary conditions. This table reports the values used for the modeling of heating and cooling control, natural ventilation, infiltration, and internal gains, which are shared across the three implemented rooms.
Figure 3: Plant of each TRNSYS model. This Figure provides an overview of the energy plant implemented in each TRNSYS room.
Surrogate
Light-weight
Medium-weight
Heavy-weight
Model
R2
nMAE
R2
nMAE
R2
nMAE
Winter
0.77
10.24
0.64
13.73
0.60
14.00
Summer
0.90
6.10
0.76
10.52
0.70
12.24
Table 3: Identification performance. This table reports, for each room configuration, the R2 and nMAE metrics achieved on the validation set by the surrogate heating and cooling models identified from the TRNSYS simulation.
Figure 4: Heating and cooling model identification. This figure shows two representative validation periods, with winter data on the left and summer data on the right. The first two rows report the model’s inputs, common to the three room configurations. The remaining rows compare the heating and cooling power predicted by the TRNSYS model ( P ) with that predicted by the corresponding surrogate model ( Ppred ).
Configuration
Hyperparameters
Emissions Reduction
Setpoint deviation
m [h]
ts [min]
ω [-]
avg per day [g/day]
tot per year [%]
avg per day [ ∘C ]
max in a day [ ∘C ]
Light-weight
12
30
106
19.67
9.88
0.1
0.5
Medium-weight
24
60
106
52.30
25.37
0.3
0.9
Heavy-weight
48
30
106
46.06
24.77
0.4
1.2
Table 4: Optimal hyperparameters set. The table reports the values of prediction horizon m , sampling time ts , and weight ω identified via grid search, along with the CO2 emission reductions and setpoint deviation.
Figure 5: Sensitivity analysis on hyperparameters. This figure shows how total yearly emissions reduction (left) and maximum daily setpoint deviation (right) change as a function of the sampling time and the prediction horizon for each room configuration. Optimal hyperparameters set is marked by a yellow star.
Figure 6: Strategy performance: annual results. This figure compares in all the considered room configurations the yearly aggregated CO2 emissions (left) and energy consumption (right) for the baseline scenario ( α(k)=0 ) and the proposed strategy.
Figure 7: Simulated scenario: input parameters. This figure shows the trend of the input variables in a zoomed window of the defined scenario.
Figure 8: Simulated scenario: output variables. This figure shows the trend of the output variables in the defined scenario, referred to a zoomed window of two weeks. Please note that ΔCO2 and ΔT are computed with respect to the baseline scenario in which no strategy is applied, i.e., ( α=0 ).
Figure 9: ω fine-tuning. This figure shows the trends of annual CO2 emissions reduction and average daily temperature deviation with respect to the weight ω .
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 10: Designed building section. This Figure shows the plant and the AA’ section of the building hosting the designed office room.
Figure 11: Light-weight configuration stratigraphy. This Figure shows the stratigraphy of the main elements constituting the light-weight version of the building hosting the designed office room.
Figure 12: Medium-weight configuration stratigraphy. This Figure shows the stratigraphy of the main elements constituting the medium-weight version of the building hosting the designed office room.
Figure 13: Heavy-weight configuration stratigraphy. This Figure shows the stratigraphy of the main elements constituting the heavy-weight version of the building hosting the designed office room.
Type
Definition
Settings
941
Blower Power = 0.059 kW
Air To Water
Total Air Flowrate = 1097.04 m3/hr
Heat Pump
Rated Heating Capacity = 1.04 kW
Rated Heating Power = 0.246 kW
954a
Total Air Flowrate = 378 m3/hr
Air to
Rated Indoor Fan Power = 0,035 kW
Appendix
Table 5: Employed TRNSYS Types. This Table lists all the TRNSYS Types used to design the considered office room and reports their parameter settings.
Scenario
Settings
PMV
Winter scenario without α
Air temperature = 20 ∘C
Air speed = 0.1 m/s
Relative humidity = 50%
PMV = -0.31
Metabolic rate = 1.2 (70 W/m2 )
Clothing insulation = 1.1 (0.17 m2K/W )
Air temperature = 21.2 ∘C
Appendix
Table 6: PMV calculation in different scenarios. The table summarizes the parameters used for the PMV calculation across the considered scenarios.
Building Energy Management coordinates HVAC, storage, and flexible loads to reduce energy use and emissions while maintaining comfort and reliable service.
Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-installation of energy storage or continued use of existing fossil dispatchable power sources to balance supply and demand. The effective CO2 emission reduction depends on the relative capacity of the energy storage and renewable sources, the stochasticity of the renewable generation, and the control of the isolated energy system. While the operation of the energy storage and dispatchable sources impacts the optimal sizing of the system, it is challenging to account for the effect of finite-horizon optimal control at the stage of system sizing. In this work, we present a flexible and computationally efficient sizing framework for energy storage and renewable capacity in isolated energy systems, accounting for uncertainty in the renewable generation and the optimal control. We implement an imitation learning approach to stochastic neural model predictive control (MPC) which allows us to relate the battery storage and wind peak capacities to the emissions reduction and investment costs while accounting for finite horizon, optimal control without solving an infeasible number of optimization problems. We evaluate the proposed sizing framework on a case study of an offshore energy system with a gas turbine, a wind farm and a battery energy storage system (BESS). In this case, we find a nonlinear, nontrivial relationship between the investment costs and the reduction in gas usage for different wind and BESS capacities.
Simon Halvdansson, Lucas Ferreira Bernardino, Brage Rugstad Knudsen
Residential battery energy storage systems (BESS) are increasingly deployed alongside photovoltaic (PV) generation to reduce household energy costs under volatile time-of-use (TOU) tariffs. Model predictive control (MPC) is a widely adopted optimisation strategy for home energy management systems (HEMS), typically formulated to minimise net energy cost, subject to physical and operational constraints. However, battery degradation is rarely embedded in the optimisation objective, meaning its cost is unquantified and aggressive; high-cycle-count strategies could incur significant losses once deployed to physical systems. This paper presents a receding-horizon mixed-integer linear programming (MILP) baseline for a UK residential HEMS, using demand data from the REFIT dataset. A 3 by 3 sensitivity study is conducted across three battery sizes and three PV array sizes, with post-hoc degradation cost estimated using the Naumann stress model and rainflow cycle counting. Results show that degradation remains constant for each battery size and can exceed energy cost savings by up to 1,060 %. These results demonstrate that energy-cost-only optimisation systematically underestimates the true system cost, motivating a degradation-aware control formulation.
Dawood Butt, Nandor Verba
WMG, The University of Warwick, Coventry, United Kingdom
Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of heterogeneous buildings. Existing tools force a hard tradeoff: high-fidelity physics simulators such as EnergyPlus are accurate but sequential and require per-building calibration, while purely data-driven sequence models scale but abandon the physical structure that makes their predictions trustworthy. We introduce NeuralBES (Building Energy Simulation), a differentiable emulator that resolves this tradeoff by parameterizing a resistance--capacitance (RC) based thermal model with a shared neural encoder: static building metadata such as floor area, vintage, and HVAC type is mapped to physically bounded capacitances, conductances, and equipment coefficients, which become the coefficients of a scalar linear recurrence solved via a log-space parallel scan, and a predictor--corrector loop closes the thermostat--temperature nonlinearity while preserving full-horizon gradient flow. Trained on the ResStock dataset across three climate zones, NeuralBES handles heterogeneous building archetypes, vintages, and climate zones within a single trained encoder, while black-box baselines produce statistically plausible but physically inconsistent trajectories. On the annual full-year rollout, NeuralBES is the only data-conditioned model that is simultaneously physics-valid and accurate to within 4 MAPE points of the strongest raw-error baseline, while operating at roughly an order of magnitude fewer parameters than the transformer and recurrent baselines; among physics-valid baselines at parameter parity it more than halves the MAPE of the grey-box RC alternative.