Loads In Commercial Buildings Research Articles

Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.

Applying indirect evaporative chillers for comfort cooling in Northern European commercial buildings: A case study in Sweden

Indirect evaporative chiller (IEC) can produce chilled water below the wet-bulb temperature of outdoor air. To evaluate the potential of applying indirect evaporative chillers for covering the high indoor sensible cooling loads in commercial buildings of Northern European countries, a case study based on a real commercial building in Stockholm was carried out assuming renovation of the existing air-conditioning system by replacing district cooling with an indirect evaporative chiller as cooling source. Numerical models were built for the indirect evaporative chiller as well as for the entire space cooling system, and techno-economic performance evaluation as well as sensitivity analyses were conducted with a validated numerical model to comprehensively evaluate renovation benefits. The simulation results show that, an indirect evaporative chiller fulfilling 80 % of the total sensible cooling load of the design outdoor condition with a dry-bulb outdoor air temperature of 26 °C and a relative humidity of 45 %, can produce chilled water below the wet-bulb outdoor air temperature when the relative humidity is lower than 0.4 for hours in July of year 2015 and 2018. Sensitivity analyses show that reducing the supply air flow rate of the ventilation system from the default setpoint of 1.2 L/(m2·s) to the minimum hygienically required level of 0.35 L/(m2·s) would double the seasonal energy efficiency rating of the air-conditioning system from 6.3 to 12.8. Compared to the original district cooling-based system, the operational expenditure of the renovated system can be saved for optimally 54 k SEK, justifying a capital expenditure of 588 k SEK assuming operation of 15 years. The case study shows that indirect evaporative chiller can potentially be applied for commercial buildings under the and climatic and market context of Sweden, providing an alternative cooling solution for similar applications.

Co-Optimization of Distributed Energy Resources Under Time-of-Use Pricing Frame

Abstract Distributed energy resources (DERs) have been considered as a promising solution due to the benefits on efficiency and environmental sides. However, despite the rapid development of distributed energy resources and technologies, the share of distributed energy generation is still small in comparison to that of traditional generation. Time-of-use (TOU) pricing can be an important incentive strategy to encourage the penetration of distributed energy resources. In this paper, a multi-objective optimization considering the time-of-use pricing impacts is proposed to determine the optimal configuration and capacity of distributed energy resources involving different technologies, including solar photovoltaic (PV), solar thermal collector (STC), combined heating and power system (CHP), and integrated energy storage (ES). The distributed energy resources are designed to satisfy the electric and thermal load of commercial buildings (large hotels and medium offices) partially or entirely. The proposed multi-objective optimization is utilized to configure the optimal combination of distributed energy technologies as well as the system capacity to reduce both the cost and environmental impact of the system in different locations. Results show that the proposed optimization method can achieve a trade-off between system cost and environmental impact based on the existing time-of-use pricing structure.

Comparative Cost and Design of Solid, Ribbed and Waffle Slab for a Three-storey Shopping Complex Using Prota, Orion and Spreadsheet

Cost is an important factor in choosing the type of floor or slab used in any construction. When a large space within a building needs to be covered without hindrance and supports, waffle or ribbed slabs are used. For all slab systems, the cost constitutes a significant portion of the overall cost of the structure. Therefore, this research aims to select a preferable slab system with the least construction cost for a shopping mall. Computer-aided design software such as Prota 2018 and Orion 2018 was used for analysis and design, and RCC spreadsheets were used to manually check the slabs. The results reveal that the Solid floor slab has more concrete volume and steel than the waffle, and the rib consumes the lowest least volume of concrete and steel among the three slab systems. Therefore, the rib slab is the most economical of the three slab systems since it uses the least concrete and reinforcement. The cost of a floor slab system ranges from 12 to 17% of the structural cost in building works, the percentages of the cost of the slab in a floor system is 17.84% for solid slabs system, 13.65% for rib slabs and 12.48% for waffle slabs of the total cost of a structure. It can therefore be concluded that using rib slab saves 17.95% and 7.46% of the construction sum when compared to solid slab and waffle slab construction systems respectively. In conclusion, it is recommended that ribbed slabs should be used for commercial building construction with more than 5KN/m2 live load in commercial buildings that spans up to 15m and more.

Role of façade shades in energy efficiency in commercial buildings

Energy consumption and production all over the world is a key contributor to climate change due to resultant emissions of CO2. To meet the national goal of achieving net zero carbon emissions by 2070, the current trends in energy consumption need to be addressed, with the building sector accounting for 40% of the global energy consumption. In India, the heating, cooling and lighting load of commercial buildings has increased from 32% to 46% in the past two decades. Energy demands can sustainably be reduced by means of energy conservation measures, particularly during the building design stage. As demands for new commercial spaces increased in crowded cities, the multi-storeyed glass façade building emerged out and continues to be a popular building design. This paper examines the role of façade shading elements in pulling down the energy demands and harnessing required daylight for lighting comfort. Daylight, energy, solar insolation and glare patterns are analysed in existing building in Noida on the basis of which different shading configurations are proposed by means of computer simulation, using Ecotect, eQUEST software and Radiance tool. Based on the analysis, three cogent designs are discussed and the optimum shade design is identified.

A universal optimization framework for commercial building loads using DERs from utility tariff perspective with tariff change impacts analysis

To accommodate the changes in the nature and the pattern of electricity consumption with the available resources, utilities have introduced variety of tariffs over the years. This paper develops a comprehensive optimization framework that addresses the challenges associated with the diversity of utility tariffs for commercial loads. Optimization of commercial building net load through Battery Energy Storage System (BESS) and renewable energy resources is modeled and explored for minimizing billing cost for different tariffs. Cost functions are formulated for each possible commercial utility tariff type, engendering the universal cost function format. An algorithm is developed to apply the optimization model and generate the desired optimal outputs (e.g., optimal net load and BESS power, costs and savings, etc.). The results for several building loads and all tariff types are compared and analyzed to present the findings. An impacts analysis is performed to observe how different changes in tariffs affect the optimizations results. Results exhibit that for same building load and renewable generation, the tariff type can have an overwhelming impact on costs and savings. Tariffs without Time of Use (TOU) charges or peak demand charge components offer lower savings opportunity from BESS optimization. Price change sensitivities showed the complicated dynamics among TOU price components on savings and historical trend analysis as an effective tool to predict future savings. Tradeoff between energy and demand charge heavy tariff options are also discussed for user benefit. The recent change in TOU time periods presents limited desired benefits while the modified tariff proposed in this paper exhibit significant improvement in terms of utility operation.

Flexible load optimal regulation strategy of commercial buildings under the environment of electricity market

Under the electricity market environment, the flexible load of power users can not only reduce the cost of power consumption by adjusting their own power consumption behaviors according to the real-time price (RTP), but also can obtain additional benefits by participating in auxiliary services. The load of commercial buildings (CBs) is concentrated and makes a greater contribution to the peak load of the power system. If the flexible load of CBs can be regulated, it has great benefits for shifting the peak load of the power system in addition to reducing the electricity cost. On the basis of analyzing the characteristics of flexible loads in CBs, a flexible load regulation model including short-time interruptible load (IL), reducible load (RL), translatable load (TL), air conditioning system (ACS) and energy storage system (ESS) is established. Considering the electrical comfort and temperature preference, the objective function is jointly constructed with the electrical cost, temperature discomfort and ancillary service income. An optimal regulation strategy for multiple types of flexible loads in CBs based on the electricity market environment to realize the economic power consumption of CBs is formulated. The simulation results show that the proposed strategy can reduce the power cost of CBs and lower the peak demand under the condition of satisfying the power consumption comfort.

Lyapunov Optimization in Online Battery Energy Storage System Control for Commercial Buildings

The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.

Application of the InTIME Methodology for the Transition of Office Buildings to Low Carbon—A Case Study

The COP21 Paris Agreement requires urgent abatement of 80% of the current fossil-based energy consumption to keep global warming below dangerous levels. Heating loads in commercial buildings can be reduced by retrofitting the building envelope, upgrading the efficiency of heating equipment, implementing energy management strategies, substituting renewable energy sources, and influencing energy-saving behavior. However, achieving the downshift of gas or coal heat is a wicked problem. The Interdisciplinary Transition Innovation Management and Engineering (InTIME) methodology was applied to address the wicked problem of district heating of campus buildings of the University of Canterbury, in Christchurch, New Zealand. The carbon downshift scenario requires a reduction in coal purchase by 80% from the first year through the engineering of adaptive measures for facility operators and occupants. Accordingly, a successful downshift of fossil-fuel energy would depend on the effective adaptation of the office workers. Adaptation plans to facilitate demand participation and sustained worker productivity could be designed once the actual heating behaviour is known. The contribution of this work is a novel fossil fuel abatement concept: the Targeted Heating Energy—Assessment and Intervention Design (THE-AID), which focuses on the assessment of the heating behavioural patterns of office workers. Building services engineers can use the THE-AID concept to develop adaptation plans through intervention design and resource facilitation focused on building occupants. THE-AID projects could achieve significant emissions reduction in the near term at a low cost and increase resilience to heat supply disruptions.

Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings

Plug load management systems are touted as promising solutions to reduce the rising energy consumption of plug loads in commercial buildings through different load monitoring and control strategies. However, their real-world applications remain relatively unexplored due to several issues related to deployment viability, energy-saving potentials, and system acceptance. Given these limitations, this paper proposes Plug-Mate, a novel IoT-based occupancy-driven plug load management system that reduces plug load energy consumption and user burden through intelligent plug load automation. The proposed system uses an interconnected network of modules and subsystems to perform plug load automation based on the users’ (1) high-resolution occupancy information obtained through a non-intrusive indoor localisation system, (2) plug load type information inferred through an advanced plug load identification feature, and (3) diverse control preferences through a personalised user interface. To demonstrate the system’s feasibility, six control strategies were evaluated during a 5-month field study in a university office space. Each control strategy involved different levels of plug load automation (i.e., manual, predefined schedules, and occupancy-driven) and was assessed based on their energy savings and user satisfaction levels to identify the optimal balance between automation and user control. Based on this evaluation, the best control strategy reported an average energy savings of 51.7% among different plug load types evaluated, achieving a 7.5% reduction in the building’s energy use and the highest user satisfaction score of 4.7 out of 5. Finally, we concluded this work by highlighting the system’s deployment feasibility for a building-wide implementation to guide future real-world applications. • A novel IoT-based occupancy-driven plug load management system is proposed. • Plug loads are automated based on occupancy, plug load type, and control preferences. • A 5-month field study was conducted to evaluate six plug load control strategies. • The system reported a 7.5% building-wide savings and user satisfaction score of 4.7/5. • The feasibility and economic viability of a building-wide deployment were discussed.

Study on the performance and economy of the building-integrated micro-grid considering photovoltaic and pumped storage: a case study in Foshan

Abstract Micro-grid is a promising technology for the energy reform in urban areas. This paper takes a building as a case study to construct a micro-grid system, which includes rooftop photovoltaic and pumped storage systems according to the characteristics of the building. Moreover, the cost–benefit analysis of the commercial building micro-grid system is also performed to obtain an in-depth understanding of the feasibility of the project. According to the characteristics of power load of commercial buildings, the working characteristics of the developed micro-grid and the mathematical model of optimal operation of micro-grid in commercial buildings is constructed. The calculation results from the mathematical model show that the dynamic payback period (PBP) of the micro-grid system of commercial buildings is 4.74 years, while the dynamic PBP of the micro-grid system of battery energy storage is 7.95 years. Therefore, the micro-grid system pumped storage has a much better economy than that with battery energy storage.

A Universal Optimization Framework for Commercial Building Loads Using DERs from Utility Tariff Perspective with Tariff Change Impacts Analysis

A Universal Optimization Framework for Commercial Building Loads Using DERs from Utility Tariff Perspective with Tariff Change Impacts Analysis

Commercial building load characteristics modeling considering equipment innate laws and various staff behaviors under demand response mechanism

Abstract At present, the traditional load model relies on a large number of historical data, ignoring the relationship between load demand and user behavior and the temporal and spatial distribution of load. To solve this problem, this paper proposes a modeling method of energy consumption characteristics of commercial office building considering the physical characteristics of energy consuming equipment and staff behavior factors. Firstly, the load of commercial office building is classified based on user behavior. Secondly, according to the proposed load classification method, a time distribution model associated with user behavior is established for each type of load energy consumption equipment, and the total load model of commercial office building is obtained. On this basis, the proposed model is expanded in both time and space, so that the total load model of commercial office building can be used to analyze energy consumption in different time scales and different regional areas. Through the combination of non-intrusive load decomposition and Markov chain, the energy consumption behavior of users is analyzed and simulated, and a refined load forecasting method of commercial office building considering equipment and user behavior under demand response mechanism is proposed. Finally, the example analysis shows that the proposed method can no longer depend on huge amounts of similar data for driving, can effectively reduce the impact of the original data on the load feature extraction, and has the ability to achieve load forecasting independently.

Time–Frequency Mask Estimation Based on Deep Neural Network for Flexible Load Disaggregation in Buildings

In this paper, a novel mask-based load disaggregation scheme is presented to extract flexible load profiles in buildings. Flexible loads are those that can be adjusted as needed and examples of such loads are heating, ventilation, and air-conditioning (HVAC), and lighting loads. Knowledge about the flexible sub-load in buildings is crucial for demand-side management programs. However, the load decomposition performance of conventional disaggregation methods may be limited mainly because similar profiles are superimposed on the entire load of the buildings. Motivated by these problems, a deep neural network (DNN)-based mask, termed DNNM, is proposed. It is customized in a time-frequency (T-F) domain to effectively extract the flexible portion of loads. To the best of our knowledge, the DNNM is to achieve load disaggregation using the distinctive T-F properties of the flexible loads. Particularly, a new mask is designed to increase the load disaggregation accuracy by determining the appropriate ratio of flexible load in the mixed loads, adaptively for different T-F elements. Numerical evaluations for residential and commercial building loads show that the proposed DNNM scheme outperforms the conventional disaggregation models in discriminating the contributions of the flexible load from the total power consumption.

Optimal Control Strategy of Platform Load Oriented to Network and Load Cooperation

In recent years, with the continuous growth of China’s power peak load and the rapid development of renewable energy, a large number of renewable energy sources are connected to the power grid, increasing the uncertainty of power grid operation and posing new major challenges to the power system regulation capacity. Flexible load has the characteristics of wide distribution, fast response, and high economy, which is an important control resource for the future power system. Based on the flexible load of commercial buildings and residential users, this paper studies the resource characteristics and response characteristics, clarifies the resource characteristics and demand response characteristic indexes of commercial and residential users, and establishes the response characteristics model of commercial buildings and residential users. Considering the influence of weather, holidays, incentive mechanism, and other factors on the response of flexible load, the quantitative analysis method of flexible load resource regulation potential for regional power grid dispatching was studied, and the feasibility of flexible load resources directly participating in the load control system was analyzed. Based on the uncertainty and mathematical characterization method of the active response of flexible loads, the optimal combination control strategy of demand response resources was proposed to eliminate the problems of heavy load and overload of regional power grid equipment by using the active response ability of flexible loads. Finally, the IEEE 14-node system is selected for simulation verification, which provides a theoretical basis for alleviating the power grid operation pressure in the peak load period of the power grid in the urban core area, improving the safety and economic operation level of regional power grid dispatching and the utilization rate of power grid equipment assets.

Predictive model of cooling load for ice storage air-conditioning system by using GBDT

Cooling load prediction can provide reliable data support for the development of low energy consumption building. It is difficult for traditional models to achieve stable prediction accuracy under different load mode when the amount of data is limited. To resolve this problem, gradient boosting decision tree (GBDT) model based on selection of input variables was proposed to predict the cooling load of commercial buildings. First, the input variables that affect prediction cooling load were determined through correlation analysis on the original data set. Then, the Pearson analysis was done on the original data to get the optimal combination of input variables. Next, the ensemble learning technology was used to build GBDT model. In order to improve the prediction accuracy of the model, the grid search method was applied to determine the hyperparameter combination of GBDT. Experimental results showed that in the high-load mode, the mean absolute error (MAE) of the GBDT model was reduced by about 24% compared with the SVM, and was reduced by about 37% compared with the DNN model. Under the general load mode, MAE was reduced by about 30% and 53% respectively. Compared with the other models, the prediction accuracy of GBDT model has been significantly improved under different load mode. The proposed cooling load prediction model can provide reliable data support for the predictive control of buildings.

Energy Efficiency Opportunities for Lighting Systems and Miscellaneous Electrical Loads in Commercial Buildings – A Review

Energy Efficiency Opportunities for Lighting Systems and Miscellaneous Electrical Loads in Commercial Buildings – A Review

New Recurrent Neural Network Architecture for Commercial Building Load Prediction Using Smart Meter and Wi-Fi Access Point Data

New Recurrent Neural Network Architecture for Commercial Building Load Prediction Using Smart Meter and Wi-Fi Access Point Data

Overcoming barriers to direct current power: Lessons learned from four commercial building case studies

While distribution systems deliver power to buildings using alternating current (AC), the loads in commercial buildings have increasingly shifted to those using direct current (DC) power. In addition, on-site generation, such as rooftop solar (which provides a DC source of electricity), is becoming more common in commercial buildings. As a consequence, numerous DC-to-AC and AC-to-DC conversions occur in commercial buildings, each of which results in wasted energy. This makes converting building electrical systems from AC to DC power an attractive energy efficiency measure. In the present study, barriers and advantages of DC power in commercial buildings were examined by interviewing personnel who worked on four commercial construction projects in Colorado. The cases, two new construction and two retrofits, varied in their level of successful implementation. The participants included general contractors, facilities managers, engineers, and financial officers. Interviews were coded for statements about people, technology, and the construction process. The results revealed that successful implementation required an owner who championed the project, along with a team that demonstrated positive motivations (e.g., enjoying a challenge, accepting uncertainty). Technological issues faced by the projects were largely due to limited availability and incompatibility of DC parts. Finally, pre-construction processes (financing, bidding, and contracting) emerged as important differentiators between successful and unsuccessful projects. These findings provide a roadmap for increasing successful implementation of DC power in commercial construction projects.

An assessment of power flexibility from commercial building cooling systems in the United States

Understanding the varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning loads in commercial buildings in the U.S. using a simulation-based method. Commercial buildings are first grouped by building type and climate region. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that cooling loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while medium office buildings have the most flexibility as a percentage of the rated power consumption.