Building Energy Demand Research Articles

Benchmarking performance of machine-learning methods for building energy demand modelling

The relevance, relative importance and colinearity of input parameters to the results of building energy demand forecasts were investigated. Two calendar years of historical data including weather variables and days of the week were used. The study also aimed to assess the performance of multiple-linear-regression, support-vector-machine and artificial-neural-network models for predicting the daily heating, ventilation and air-conditioning energy consumption of a commercial building in France. Mean absolute error, root-mean-square error and coefficient of variation of root-mean-square error were selected as the performance criteria. The results showed that the best performance was achieved through the artificial-neural-network model according to all performance measures. Besides, the other two models were not able to meet the prediction requirements for energy consumption in a building since their coefficient-of-variation-of-root-mean-square-error values were not below 30%. The results also indicated that there was multiple colinearity between the number of degree days and outdoor temperature. Furthermore, the most significant parameter for daily energy consumption was found to be the number of degree days, followed by global radiation, sunshine rate and the day of the week.

CFD modeling of the building integrated with a novel design of a one-sided wind-catcher with water spray: Focus on thermal comfort

The rising energy demand for buildings has enhanced public awareness of sustainable energy sources and technologies. In particular, natural ventilation systems such as wind-catchers have attracted considerable new attention. A new wind-catcher design with single-stage direct-air evaporative cooling was proposed for indoor air conditioning. An Eulerian-Lagrangian approach employing the Realizable k-ε model was utilized to conduct the CFD simulations. Furthermore, the effects of inclining the bottom surface of the wind-catcher and installing a baffle across the flow path on the air temperature drop, water mass fraction, and air velocity distribution were studied. The inclined bottom surface led to more flow uniformity in the room compared to the conventional geometry. The baffled wind-catcher with β = 0, 30, 45, and 60° and unbaffled wind-catcher showed different flow patterns and thermal comforts. A methodology for evaluating the thermal comfort performance of evaporative cooling systems integrated into natural or passive cooling devices was also proposed based on the generated CFD results. The baffled wind-catcher with β = 60° combined with an evaporative cooling system significantly reduced the air temperature inside the building up to 17.4 °C and improved the occupants’ thermal comfort. The most suitable design for thermal comfort was also determined.

Analysis of the energy and visual performance of a building with photochromic windows for a location in southern Italy

Windows have a considerable influence on the thermal balance of buildings. The aim of the present work is to evaluate the performance of a photochromic window (PCW) in terms of building's energy demands and natural lighting. In these windows, a fundamental role is played by the solar transmission coefficient, which can change depending on the incident solar radiation. A comparison is made with the case of using clear double glazing and low emissivity (Low-E) windows. The analysis is performed using EnergyPlus, where a laboratory of DIMEG, University of Calabria (Rende, Italy) is modelled. The model is calibrated to be representative of reality and can be used to compare different types of windows under the same external conditions. Results are obtained for a south-facing window, varying its size. The results show, for this location in southern Italy, that the PCW allows a reduction in annual energy consumption compared to the clear window (up to 9.3%) and compared to the Low-E window (up to 4.1%). The daylight provided by the PCW is greater than the other two solutions and, in some cases, also reduces the risk of glare.

A Methodology of Creating a Synthetic, Urban-Specific Weather Dataset Using a Microclimate Model for Building Energy Modelling

The relationship between outdoor microclimate and indoor building conditions requires the input of hourly weather data on the typical meteorological characteristics of the specific location. These data, known as typical meteorological year (TMY), are mainly deduced from the multi-year records of meteorological stations outside urban centres, preventing the actual complex interactions between solar radiation, wind speed, and high urban density. These factors create the urban heat island effect and higher ambient air temperatures, skewing the assumptions for energy demand in buildings. This paper presents a computational method for assessing the effect of the urban climate in the generation of typical weather data for dynamic energy calculations. As such, the paper discusses an evaluation method of pairing ENVI-met 4 microclimate and IES-VE building energy modelling software to produce a typical urban specific weather dataset (USWDs) that reflects the actual microclimatic conditions. The ENVI-met results for the outdoor microclimate conditions were employed to determine the thermal boundaries for the IES-VE, and then used to compute the building’s energy consumption. The energy modelling that employed the USWDs achieved better performance compared to the TMY, as the former had just a 6% variation from the actual electricity consumption of the building compared to 15% for the latter.

Dual-Layered Titanium Oxide-Acrylic Film on Carbon Black for Passive Radiative Cooling below Ambient Temperature under Direct Solar Irradiance

Cooling is an energy-intensive process; and with the effects of global warming, space conditioning poses more load on the scarce and costly energy available for consumption in facilities. Thus, the use of a passive sub-ambient diurnal radiative cooler, which is an effective natural space cooling technology, can help reduce the total energy demand for buildings. A diurnal passive radiative cooler of 40 × 40 cm, has been designed, fabricated, and experimentally investigated. The cooler comprises a photonic solar reflector and thermal emitter made of titanium dioxide embedded in an epoxy resin that reflects 96% of insolation and emits strongly in the 8-13 µm atmospheric window. Design calculations were made under Owerri climatic conditions. The results obtained showed that when the cooler was exposed to direct insolation well above 950 W/m2 , it achieved a drop in temperature of 1-2 °C below ambient between 6 am and 9 am. As the solar radiation increased, the temperature of the cooler increased until it reached 41 °C, which was above the ambient air temperature (30 °C). The cooler temperature decreased as the solar radiation decreased, dropping below the ambient temperature from 5:45 pm, by 3 °C during the remaining hours of the investigation. The photonic radiative cooler has an estimated cooling power of 56.8 W/m2 under a clear night sky and achieved sub-ambient cooling during the early and late hours of the day under low solar radiation. Therefore, passive cooling through the photonic approach offers prospects for energy efficiency. Further works may have the prospects of achieving improved passive sub-ambient daytime cooling.

Net energy and cost benefit of phthalocyanine and heptamethine transparent photovoltaics in commercial buildings

There is a growing interest in using transparent organic photovoltaics (TOPVs) to generate on-site electricity and reduce building energy demand. This study evaluates the use of chloroaluminium phthalocyanine and heptamethine-based TOPVs in five types of commercial buildings and four climates in the U.S. The building energy demand is modeled with and without TOPVs, along with the electricity generation from the TOPVs, and the cradle to gate life-cycle cumulative energy demand to manufacture the TOPV modules on glass. The net energy benefit is positive for all five commercial buildings in all four locations meaning that more energy is produced or saved than used in manufacturing TOPVs. The energy payback times for TOPVs are less than other building integrated PV technologies. All commercial buildings save energy, cost, and greenhouse gas emissions with TOPVs, and the best applications depend on the building’s window-wall ratio, occupancy, and usage. In the future, the spectral properties of TOPVs could be tailored to save more cooling and heating energy based on the building’s structure, climate, and energy demand.

Energy Efficiency in High-rise Office Buildings: An Appraisal of its Adoption in Lagos, Nigeria

High-rise office buildings are naturally energy-intensive as energy is required in large quantities to run modern building services and to power equipment needed for a hitch-free operation of the buildings. It was found in studied literature that maintaining good indoor environmental quality through air-conditioning, lighting and powering of office equipment contribute the most to an office building’s total energy consumption. Hence, over time, various strategies have been employed to reduce the intense energy demand in high-rise office buildings. This paper adopted the use of both literature review and case study methods. The paper identifies the key energy efficiency strategies that have been successfully deployed in high-rise office buildings using the literature review approach. Also, case studies were conducted on three relatively new high-rise office buildings in Lagos, Nigeria by evaluating them against the background of the best practices in energy efficiency. The study found that deployment of energy efficiency strategies in high-rise office buildings in the study area is still very low especially in the areas of building orientation, building envelope design and the use of renewable energy. However, the use of day-lighting techniques and sustainable lighting systems are quite prominent in the office buildings studied. The study underscores the need to make high-rise office buildings more sustainable through energy efficiency strategies across the whole building life cycle of design, construction, use and end of life.

Machine learning to predict building energy performance in different climates

Digitalization is sweeping the world of buildings. Notably, the use of machine and deep learning techniques to develop buildings’ digital twins is becoming crucial to foster the energy transition of the construction sector and a sustainable urban growth. Digital twins can ensure a user-friendly, fast and reliable prediction of building energy loads and demands, thereby enabling a comprehensive optimization of planning, design and operation. Accordingly, this study investigates machine learning techniques to predict heating loads of a building in Rome (Italy, Mediterranean conditions, “Csa” climate in the Köppen and Geiger classification) and in Berlin (Germany, European backcountry, “Cfb”). Firstly, the real building, located in Benevento, is used to develop the artificial neural networks (ANNs), then implemented in MATLAB® to achieve meta-models of building energy behavior. NARX (nonlinear autoregressive model with exogenous inputs) networks are used and trained based on simulated data, provided by the well-known building simulation tool EnergyPlus using the software DesignBuilder® as interface. The meta-model inputs are related to weather conditions, while the required outputs concern the thermal energy load for space heating. The analysis is performed with reference to annual forecasts of energy demands. In all cases, the ANNs architecture is optimized to achieve the best fitness with EnergyPlus outputs. The results show that machine learning can be a precious and reliable tool to support energy design and operation of different buildings in different climates. Nonetheless, the meta-modeling procedure needs to be properly conducted by experts to set suitable frameworks and hyperparameter values of the ANNs, as well as to achieve a right and comprehensive interpretation of the results.

Impact of urban heat island on cooling energy demand for residential building in Montreal using meteorological simulations and weather station observations

Urban heat island (UHI) and the increased frequency of heatwaves due to climate change reduce thermal comfort inside buildings leading to increased use of air conditioning systems. In this research, the impact of the actual local climate on the cooling energy demand of residential buildings in Montreal is studied. Building energy simulations (BES) are conducted for microclimates at eight locations in the Montreal area and compared with a reference weather data. The climate data for four locations are provided by weather stations. The other climate data are simulated with a detailed weather research and forecasting (WRF) model at 250 m resolution with detailed land-use data over a period of four months during the summer of 2020. The air temperatures from meteorological mesoscale simulations are validated with available weather station observations. The BES results show an increase in the cooling energy demand due to higher air temperatures at urban locations compared to the rural periphery. The cooling energy demand varies significantly over the eight locations although being within a radius of 20 km. The mean variation in cooling energy demand between the locations amounts to 14 % compared to the average cooling demand. Using the reference climate data provided in BES, the cooling energy demand is significantly underestimated by 25 % to 34 % on average. Increasing the thermostat cooling setpoint with 1 °C results in a reduction in mean cooling energy demand of 4.5 kWh/m2, or 11.7 %. A linear trend is found between the cooling energy demand from BES and cooling degree hours (CDH) indicating CDH can be used as a first indication for building energy demand.

Comparison Study of the Heating Energy Demand for a Multi-Storey Residential Building in Romania Using Steady-State and Dynamic Methods

The purpose of this study is to determine the differences between the steady-state energetic method and the dynamic energetic method in a multi-storey residential building in Romania. For both methods, there are two values for the heating energy demand, one obtained with the theoretical U value and g value according to Romanian Methodology Mc 001/1-2006 and one with the real U value and g value obtained from in situ measurements. The results of our study revealed a difference between the steady-state method and the dynamic method in both cases of approximately 20%. Because the heating energy demand needs to decrease in value according to European legislation and the classical energy demand determination is shallow, as it does not take into account some important factors, it is important to use a method that produces accurate values so the economic factor does not become overwhelming.

Design for energy flexibility in smart buildings through solar based and thermal storage systems: Modelling, simulation and control for the system optimization

The present study investigates the use and implementation of energy efficient measures and strategies for building applications, toward the Nearly Zero Energy Buildings target. Specifically, objective of the study is to implement building integrated photovoltaic thermal devices coupled with a phase change materials heat exchanger acting as an active thermal storage building component, with the aim to add flexibility to the building while still maintaining indoor comfort conditions. To show the potentials of the novel configuration proposed in this paper, a multi-zone grey-box model is developed and validated to capture the thermal dynamics of a building, and a control strategy applied to the whole system is developed for energy management purpose. The whole simulation model, including thermophysical properties of the building-system and the control features, is implemented in a MATLAB environment. To assess the model and application potentials toward the optimal design and operation of the proposed system for energy efficiency and flexibility goals, a suitable case study analysis is conducted. Thus, a sensitivity analysis, using an evolutionary algorithm, is performed by considering economic and energy objective functions which focuses on the reduction of the building energy demand, load variability and economic aspects. In this regard, the optimal design configuration is underlined in a way that the operation of the components can be maximized to provide flexibility to the building: in average working conditions one single layer of PCM can provide around 186.3 Wh/K per unit of temperature and width. A rule-based management strategy is proposed to prove the possibility to shift and shave the energy peaks during high energy request periods, demand response events. Finally, by considering an approximate economic calculation, the simple payback, taking into account only the positive effects on the winter management, is around 13.5 years.

Modelling of a multi-stage energy management control routine for energy demand forecasting, flexibility, and optimization of smart communities using a Recurrent Neural Network

This paper proposes an innovative algorithm for community energy management control, able to involve customers in energy trading by exploiting their potential energy flexibility. The main innovation relies on a matrix-based control system where the strategy considers individual and community priorities simultaneously. Through the individual energy flexibility and the community energy pool, the aggregated network energy supply is controlled and shaped. The presented model presents a generalized structure based on control volumes, and it can be universally applied to energy communities of different sizes, number of participants, energy carriers, penetration of photovoltaics, and electric vehicles. The predictive system is conceived from a recurrent neural network, which performs a real-time prediction on energy demands in buildings. Suitable energy flows optimization is also presented with different implications for economic and energy savings. Finally, to show the potential of the developed model, a suitable case study analysis is presented. Important results include the achievement of a typical win-win condition, where both the distribution system operator and final customers benefit from this strategy. Specifically, a reduction of energy demand during demand response events of about 21% is achieved, whereas the interaction with the electricity network decreases of about 15%.

Comparative performance evaluation of smart reversible thermochromic pigment-based cement and polymeric mortars

Use of thermochromic (TC) materials has demonstrated benefits in reducing the energy demand of buildings. The present study reports the performance comparison of smart reversible thermochromic cement and polymeric mortars. For this purpose, cement, epoxy, and polyester mortars were fabricated with and without thermochromic pigment. After that, the properties such as water absorption, compressive strength, and ultrasonic pulse velocity (UPV) were assessed for all specimens. In addition, analytical characterization, i.e., X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy dispersive X-ray (EDX) analyses on the mortars were performed. The color stability, solar reflectance, and temperature tests on mortars after exposure to control and natural environments for 28 and 180 days, respectively, were also performed. The results revealed that compared with thermochromic cement mortars, the thermochromic polymeric mortars retain their color, solar reflectance, and temperature regulation properties, leading to a lower diminution in the control and natural environment. The SEM analysis suggests that the TC pigment microcapsules were firmly embedded in the polymeric matrix leading to a stable chromatic response of mortars. On the other hand, the harsh alkaline cementitious environment leads to degradation and deterioration in the chromatic response of TC pigment. Furthermore, polymeric thermochromic mortars exhibited higher compressive strength, ultrasonic pulse velocity, and lower water absorption values than cement-based thermochromic mortars. Hence, polymeric mortars are more suitable for fabricating smart thermochromic composites.

Heat distribution concepts for small solar district heating systems – Techno-economic study for low line heat densities

• Two novel distribution concepts are compared to conventional district heating. • A novel distribution system with central domestic hot-water heating is presented. • Hybrid distribution outperforms a conventional steel pipe system. • A complete low-temperature system outperforms hybrid distribution. • Reduction in line heat density favours a low-temperature GRUDIS system. The high operating temperatures in today’s district heating networks combined with the low energy demand of new buildings lead to high relative network heat losses. New networks featuring lower operating temperatures have reduced relative heat losses while enabling an increase in the use of solar heat. The primary aim of this study was to determine if a particular district heating system can be made more effective with respect to heat losses and useful solar energy, by considering different distribution concepts and load densities. A small solar assisted district heating system with a novel hybrid distribution system has been modelled based on a real case study. This model serves as a basis for two other models where the distribution system and heating network operating temperature is changed. A secondary aim of the study was to determine the economic implications of making these changes, by using costs estimates to calculate the contribution of essential system components to total system cost. Results indicate that a novel distribution concept with lower network temperatures and central domestic hot water preparation is most energy efficient in a sparse network with a heat density of 0.2 MWh/m∙a and a performance ratio of 66%, while a conventional district heating system performs worst and has a performance ratio of less than 58% at the same heat density. In an extremely sparse network with heat density of 0.05 MWh/m∙a, the performance ratio is 41% and 30% for these systems, respectively. A simple economic analysis indicates that the novel distribution concept is also best from an economic point of view, reducing the initial investment cost by 1/3 compared to the conventional concept, which is the most costly. However, more detailed calculations are needed to conclude on this.

Conversion of landfilled waste-to-electricity (WTE) for energy efficiency improvement in Shenzhen (China): A strategy to contribute to resource recovery of unused methane for generating renewable energy on-site

Recent growth of population and rapid urbanization have increased the generation of municipal solid waste (MSW) in Shenzhen (China). As a result, the city strives to implement circular economy (CE) by converting its landfilled waste into electricity. As a primary component in landfill gas (LFG), methane (CH4) emissions need to be mitigated to deal with climate change. This exploratory study investigates the utilization of LFG based on CH4 formation at a waste-to-energy (WTE) plant in Shenzhen (China) by converting landfilled waste into electricity. This work also explores the scheme of incorporating a combined cooling, heating, and power (CCHP) system into the WTE power station by utilizing the waste heat of a LFG power generation process. To maximize LFG utilization efficiency and optimize the return on investment (ROI) in the city's WTE, an economic viability of energy generation that incorporates a CCHP system into the plant is presented. The WTE's capacity for power generation and total energy provided by waste heat utilization are estimated. The benefits to local community and the project's long-term impacts on the environment are elaborated. It was found from modeling study that about 2.22E+11 kg of landfilled MSW during a 15-year period yielded 1.34E+10 kg of CH4, while 90% of CH4 production still occurred about 20 years after landfilling. During the 20-year of timespan, when harnessing the waste heat from power generation, the landfilled MSW in 2021 could generate 9.68E+8 kWh of electricity and 1.75E+13 kJ of heating, or 1.17E+13 kJ of cooling. The outputs can meet the energy demands of Shenzhen's urban buildings and its population for electricity, cooling, and heating. This implies that incorporating technological values to the landfilled waste for electricity generation not only promotes a CE, but also facilitates resource recovery from unused waste, thereby contributing to the UN Sustainable Development Goals (SDGs) by 2030.

Design and Optimization of Energy Consumption for a Low-Rise Building With Seasonal Variations Under Composite Climate of India

Abstract The need for maintaining thermal comfort conditions inside the building is a significant contributor to the total energy requirements of the buildings. Thus far, limited research has offered some strategies to mitigate the effect of ambient conditions on the thermal comfort and the building energy requirements for composite climatic zone in India. In order to address this problem, this study was conducted to evaluate and analyze the impacts of four different passive design strategies, i.e., insulation, cool roof, phase change material (PCM) thermal storage system, and shading, on thermal comfort and energy demand in the two-storey building situated in the composite climate zone of Delhi, India. The results obtained by numerical simulation for four different cities, i.e., Delhi, Jaipur, Lucknow, and Indore, have been compared to study the effect of local climatic and seasonal variations within the composite climatic zones. The simulations were conducted using the Design Builder software to capture results for one year. The results indicate that no single passive design strategy is sufficient to maintain comfortable conditions inside the building. The cool roof provides significant benefits in combination with other passive technologies in all seasons except for December, January, and February. The combination of insulation and PCM is useful for winter conditions. Energy saving of up to 20.5% is possible using the combination of all four passive strategies, whereas the cool roof alone gives an 18% reduction in energy load. The PCM with a melting point of about 32 °C is suggested for the cities under study.

A Comprehensive Evaluation Model on Optimal Operational Schedules for Battery Energy Storage System by Maximizing Self-Consumption Strategy and Genetic Algorithm

Building an energy storage system is beneficial when solar panels are not producing sufficient energy. However, there is a major issue in terms of feasibility and efficiency. These limitations could be overcome by the deployment of optimal operational strategies. In previous studies, researchers typically focused on finding problem-solving strategies in such situations with only one or two evaluation indicators, lacking a comprehensive evaluation of the integrated objective. Moreover, few studies propose a general model of battery systems suitable for forecast-based operation scenarios with different energy demand features. Therefore, this study developed a comprehensive evaluation model for the operational schedule optimization of a battery energy storage system with a detailed and holistic analysis as well as practicality in implementation. In order to consume the maximum allowable rate of PV generation as promptly and completely as possible, this model was based on a maximizing self-consumption strategy (MSC). A genetic algorithm was applied to time match PV generation and load demand with full consideration of comprehensive techno-economic indicators and total operation cost as well. The model was validated within a typical American house to select the best battery system according to techno-economic indicators for the three types of batteries analyzed. It was discovered that the three types of batteries including Discover AES, Electriq PowerPod2 and Tesla Powerwall+ could all be considered as options for energy storage, and there exist subtle differences in their technical performance during the short charging and discharging phases. Discover AES has the advantage of using PV generation in a timely manner to suit load demand during the long-term operation of a battery energy storage system. With the proper prediction of building energy demand by means of a machine learning approach, the model’s robustness and predictive performance could be further extended. The machine learning approach proved feasible for adapting our optimization model to various battery storage scenarios with different energy demand features. This study is novel in two ways. Firstly, hierarchical optimization was conducted with a genetic algorithm using the MSC strategy. Secondly, the machine learning approach was applied in conjunction with the genetic algorithm to perform online optimization for the predictive schedule. Additionally, three main advantages of the methodology proposed in this paper for producing an optimal operational schedule were identified, which are as follows: generic applicability, convenient implementation and good scalability. However, the charging and discharging performance of the battery energy storage system was simulated under short-term operation with regular solar radiation. Long-term operation considering solar fluctuation should be investigated in the future.

Heat transfer performance of a device integrating thermosyphon with form-stable phase change materials

Phase change materials (PCM) are applied worldwide as a thermal energy storage technology to reduce energy demands in buildings and solve environmental contamination issues. Form-stable phase change materials (FSPCM), as one branch of PCMs, can be improved by embedding them with thermosyphon, resulting in a better thermal performance. In this paper, a novel thermoplastic elastomer-based FSPCM was developed and tested. A device integrating FSPCM with thermosyphon was created, and the heat transfer mechanism of the unit was studied. The numerical model was established, and experiments were conducted accordingly. The average relative error between the experimental data and the model predictions was <3 %. Furthermore, a parameter study was conducted to investigate the effects of several essential factors. As a result, the evaporator length and PCM thermal conductivity were found to significantly influence the unit's heat transfer rate and overall thermal performance. However, the impact of the latent heat of the FSPCM on the heat transfer rate was negligible, for which it takes 1170 s, 1230 s, and 1394 s to finish changing phase with 136 kJ/kg, 160 kJ/kg, and 200 kJ/kg, respectively. This paper provides insights on the performance of thermosyphon integrated form-stable phase change materials and discusses its relevance for thermal energy storage applications.

ECONOMIC ASSESSMENT OF INCREASING THERMAL PROTECTION OF MUNICIPAL BUILDINGS TO MODERN EUROPEAN REQUIREMENTS

According to the plan to reduce carbon dioxide emissions in Ukraine, there is a need to replace traditional energy sources and, accordingly, types of fuel. The paper highlights the main differences between the current regulatory documents in the field of energy efficiency of buildings, analyzes the dynamics of the energy demand of a public building during its thermal modernization to the normative indicators in force in Ukraine and Europe, and the regulation of the heating and ventilation schedule. The energy consumption assessment is based on the results of calculations in accordance with the current standard DSTU A.2.2 - 12:2015 and the building model in the DesignBuilder software environment. Using DesignBuilder helps evaluate new and existing building environmental performance, energy and comfort, HVAC, daylighting, cost, design optimization, CFD, BREEAM/LEED credits and reports that meet multiple national building codes and certification standards.&#x0D; The influence of the economic indicators of Ukraine and Europe on the overall sensitivity of the project to increase the thermal protection of a public building is studied, taking into account the historical increase in energy prices in the countries under consideration. The work is the basis for further analysis and development of the concept of buildings with almost zero energy consumption in Ukraine.

Inefficient Building Electrification Will Require Massive Buildout of Renewable Energy and Seasonal Energy Storage

Building electrification is essential to many full-economy decarbonization pathways. However, current decarbonization modeling in the United States (U.S.) does not incorporate seasonal fluctuations in building energy demand, seasonal fluctuations in electricity demand of electrified buildings, or the ramifications of this extra demand for electricity generation. Here, we examine historical energy data in the U.S. to evaluate current seasonal fluctuation in total energy demand and management of seasonal fluctuations. We then model additional electricity demand under different building electrification scenarios and the necessary increases in wind or solar PV to meet this demand. We found that U.S. monthly average total building energy consumption varies by a factor of 1.6×—lowest in May and highest in January. This is largely managed by fossil fuel systems with long-term storage capability. All of our building electrification scenarios resulted in substantial increases in winter electrical demand, enough to switch the grid from summer to winter peaking. Meeting this peak with renewables would require a 28× increase in January wind generation, or a 303× increase in January solar, with excess generation in other months. Highly efficient building electrification can shrink this winter peak—requiring 4.5× more generation from wind and 36× more from solar.