Building Load Profiles Research Articles

Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts

Long-term forecasts of the aggregate electric load profile are crucial for grid investment decisions and energy system planning. With current developments in energy efficiency of new and renovated buildings, and the coupling of heating and electricity demand through heat pumps, the long-term load forecast cannot be based on its historic pattern anymore. This paper presents part of an on-going work aimed at improving forecasts of the electric load profile on a national level, based on a bottom-up approach. The proposed methodology allows to account for energy efficiency measures of buildings and introduction of heat pumps on the aggregated electric load profile. Based on monitored data from over 100 non-residential buildings from all over Norway, with hourly resolution, this paper presents panel data regression models for heat load and electric specific load separately. This distinction is crucial since it allows to consider future energy efficiency measures and substitution of heating technologies. The data set is divided into 7 building types, with two variants: regular and energy efficient. The load is dependent on hour of the day, outer temperature and type of day, such as weekday and weekend. The resulting parameter estimates characterize the energy signature for each building type and variant, normalized per floor area unit (m2). Hence, it is possible to generate load profiles for typical days, weeks and years, and make aggregated load forecasts for a given area, needing only outdoor temperature and floor areas as additional data inputs.

A method for estimating electricity consumption patterns of buildings to implement Energy Management Systems

This paper proposes a method to determine, through mathematical estimations, the electrical load profile and consumption patterns of buildings, which can be applied in Energy Management Systems (EMSs). In the first stage of the method, the architectural and operating information of the building was collected to integrate its real behavior into the proposed model. Such data were gathered via a walk-through visit around the building, an interview about staff's behavior, and the determination of characteristic demand values to identify the electrical loads of the building. The latter were categorized into general and independent loads according to their location, service, and number of users. Subsequently, the estimated load profile was validated with results of on-site measurements of the power consumption recorded in the main distribution board of a building on the campus of a higher education institution. Additionally, the influence of the hourly occupancy level on consumption patterns was determined. The results showed a close relationship between consumption patterns and hourly occupancy. The ranges of maximum and minimum demand of the measured and calculated data exhibited a high level of similarity. Furthermore, the proposed method is useful to identify the percentage of participation of different spaces and electrical loads in the total consumption of the building.

Reducing emissions from consumer energy storage using retail rate design

Minimizing retail electricity costs via demand charge management and energy arbitrage is a common application of behind-the-meter energy storage systems (ESS). Research suggests that ESS tend to increase grid emissions, but some speculate that retail rate design could lessen its impact. This paper tests that theory. In this analysis, we pair five years of historic data from ISO New England (ISO-NE) and the PJM Interconnection with 15 commercial building load profiles to reveal how different rate designs influence emissions from ESS.We find that demand and energy charges can be configured to lessen the emissions impact of ESS in some markets and reduce net system emissions in others. Minimizing different types of demand charges requires different quantities of ESS dispatch: some can be minimized in hours while others require days. Time-varying energy charges increase dispatch compared to flat charges because the ESS is used for demand charge management and energy arbitrage. Separately, time-varying energy charges reduce emissions per MW h-stored compared to flat charges, all else equal. Unfortunately, a rate design that minimizes emissions in one market might increase emissions if implemented in another. This confounds the creation of universal solutions and highlights the need for approaches tailored to specific markets.

Solar-plus-storage economics: What works where, and why?

This paper explores the economics of solar-plus-storage projects for commercial-scale, behind-the-meter applications. It provides insight into the near-term and future solar-plus-storage market opportunities across the U.S. We explore the impacts of location, building load profile, technology cost, utility rate structure, and policies on solar-plus-storage economic viability, and identify which factors are most significant to project economics. While savings from storage-only projects are largely derived from demand charge reductions, solar combined with storage also provides significant energy charge savings. A common assumption is that load profiles with peaks are likely candidates for savings from storage, due to the opportunity for demand charge reduction. Our results indicate that potential for savings from combining solar with storage is independent of building load variability, likely due to the energy cost reductions from the solar. Systems are more often economical under time of use and demand charge rates, particularly when demand charges are >$10 per kilowatt. Where systems were found to be economical, expected lifetime savings averaged between 7%–10%, with savings of 30% in numerous cases. Near term markets exist for solar-plus-storage in locations such as California and New York. As technology prices drop, the number of building types that can benefit increase, and additional markets appear in Colorado, New Mexico, and Alaska. All data from the study and interactive modeling results are available at: https://openei.org/wiki/Solar+Storage.

A Simulation-Based Policy Improvement Method for Joint-Operation of Building Microgrids With Distributed Solar Power and Battery

This paper investigates the joint-operation of building microgrids with distributed solar power and storage battery. The purpose of this paper is to improve the energy efficiency of the building microgrids taking advantage of the complementary building load profiles. Three major contributions are made in this paper. First, in order to incorporate the uncertainties in both the building load and solar power as well as in the communication system, the operation problem is formulated as a finite-stage event-based optimization model. Second, an on-line simulation-based policy improvement method is developed to improve the given control policy and an action aggregation method is applied to accelerate the computational speed. Third, a balanced battery operation strategy considering the expected life cycles is proposed. Numerical examples based on the building microgrids in a university campus are used to demonstrate the effectiveness of the proposed method in improving the building energy efficiency. The computational time in a practical system is also analyzed.

Battery life-cycle optimization and runtime control for commercial buildings demand side management: A New York City case study

In metropolitan areas populated with commercial buildings, electric power supply is stringent especially during business hours. Demand side management using battery is a promising solution to mitigate peak demands, however long payback time creates barriers for large scale adoption. In this paper, we have developed a design phase battery life-cycle cost assessment tool and a runtime controller for the building owners, taking into account the degradation of battery. In the design phase, perfect knowledge on building load profile is assumed to estimate ideal payback time. In runtime, stochastic programming and load predictions are applied to address the uncertainties in loads for producing optimal battery operation. For validation, we have performed numerical experiments using the real-life tariff model serves New York City, Zn/MnO2 battery, and state-of-the-art building simulation tool. Experimental results shows a small gap between design phase assessment and runtime control. To further examine the proposed methods, we have applied the same tariff model and performed numerical experiments on nine weather zones and three types of commercial buildings. On contrary to the common practice of shallow discharging battery for preventing phenomenal degradation, experimental results show promising payback time achieved by optimally deep discharge a battery.

Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts.

Data-Driven Charging Strategy of PEVs Under Transformer Aging Risk

Big data analytics and plug-in electric vehicle (PEV) are the important elements of smart grids in the future. This paper introduces a data-driven charging strategy for PEV-based taxis, where driving behaviors of taxis and load profiles of buildings are characterized by data analysis to make the risk-averse decision on PEV charging. First, the framework of data driven risk-averse PEV charging is introduced, where a stochastic game model is proposed. Specifically, the pricing mechanism-based charging cost and the conditional value-at-risk (CVaR) measurement are used to determine the objective function for each PEV. Then, the existence of a generalized Nash equilibrium and its seeking algorithm is studied. The convergence analysis of the algorithm is also given. Second, the big data analysis of the statistical information about PEV-based taxis and the load profile of buildings are presented by applying various data process techniques. Finally, the performance of the method is numerically illustrated by the case study via real global positioning system information of 490 PEV-based taxis and the smart meter data from local commercial buildings.

A Two-Layer Framework for Quantifying Demand Response Flexibility at Bulk Supply Points

Demand response (DR) currently plays a significant role in the operation of the electric grid. As a result, quantification of DR flexibility is an important aspect in the utilization of various DR resources. Generally, the evaluation of DR flexibility at bulk supply points (BSPs) is a challenging problem, especially without the monitoring of downstream customers’ load profiles in some areas. To solve this problem, we develop a two-layer DR flexibility estimation framework. In the top layer, a top-down optimization approach is proposed to disaggregate the BSP load into different building categories based on a suite of prototype building (PB) load profiles. In the bottom layer, simplified DR estimation models are deployed to quantify the theoretical DR flexibility of each PB type. Key advantages of this framework include: 1) quantifying DR flexibility at BSPs without relying on smart meter data or detailed customer surveys and 2) providing day-ahead, hour-ahead, and near real-time prediction of DR resources based on weather forecasts and other data. Case studies demonstrate the effectiveness of load disaggregation and DR flexibility quantification at a BSP. The prediction is compared with detailed physical models, and the mean relative errors for upper/lower DR capacity at the BSP are 1.5% and 3.1%, respectively.

Load Profile of Typical Residential Buildings in Bulgaria

In the current market design, the increasing use of renewable energy sources for electricity generation leads to new challenges in balancing supply and demand. While households are responsible for 29% of total electricity demand in Europe, a good understanding of their consumption and load profiles is missing. Households have become one of the most crucial factors shaping the management of developments towards sustainability. The development and implementation of effective policies for promoting energy efficiency in the household sector has been an emerging target of the EU. Recent analyses of Bulgarian households show this type of housing as the most statistically significant variable to impact electricity savings. This study deals with the statistical analysis of electric consumption. Statistical evaluations of electricity consumption in a typical apartment building have been obtained in the research. Mathematical models of the investigated loads have been developed. The research is based on the periodic results and the object of investigation is a typical residential block of flats in Ruse, Bulgaria. The average daily energy consumption during the weekdays and weekends is presented and analysed in the paper.

Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks

Growing home comfort is causing increasing energy consumption in residential buildings and a consequent stress in urban medium and low voltage distribution networks. Therefore, distribution system operators are obliged to manage problems related to the reliability of the electricity system and, above all, they must consider investments for enhancing the electrical infrastructure. The purpose of this paper is to assess how the reduction of building electricity consumption and the modification of the building load profile, due to load automation, combined with suitable load control programs, can improve network reliability and distribution efficiency. This paper proposes an extensive study on this issue, considering various operating scenarios with four load control programs with different purposes, the presence/absence of local generation connected to the buildings and different external thermal conditions. The study also highlights how different climatic conditions can influence the effects of the load control logics.

A Simple Metric for Predicting Revenue from Electric Peak‐Shaving and Optimal Battery Sizing

AbstractA major use case for behind‐the‐meter (BTM) electricity storage is peak‐shaving for commercial and industrial customers who must pay peak‐demand charges. Quantifying the value proposition for individual customers currently requires an optimization model, the development of which can be costly in human and computing resources. We disclose here a simple econometric model to predict revenue from retail peak‐shaving. Geared toward electric utilities, third‐party storage providers, and consumers, this model eliminates the need to formulate a model in specialized optimization software. The model is based on a predictive metric that is derived from the building's load profile. During model fitting, we discovered that the revenue estimates generated are independent of the power capacity of the battery if the maximum power‐to‐energy ratio of the storage is held constant. This effect can be used to calculate the profit‐maximizing storage size, which we explore in a case study.

Mining typical load profiles in buildings to support energy management in the smart city context

Mining typical load profiles in buildings to drive energy management strategies is a fundamental task to be addressed in a smart city environment. In this work, a general framework on load profiles characterisation in buildings based on the recent scientific literature is proposed. The process relies on the combination of different pattern recognition and classification algorithms in order to provide a robust insight of the energy usage patterns at different levels and at different scales (from single building to stock of buildings). Several implications related to energy profiling in buildings, including tariff design, demand side management and advanced energy diagnosis are discussed. Moreover, a robust methodology to mine typical energy patterns to support advanced energy diagnosis in buildings is introduced by analysing the monitored energy consumption of a cooling/heating mechanical room.

Analytical sizing methods for behind-the-meter battery storage

In behind-the-meter application, battery storage system (BSS) is used to reduce a commercial or industrial customer's payment for electricity use, including energy and demand charges. The potential value of BSS in payment reduction and the optimal size can be determined by formulating and solving standard mathematical programming problems. In such mathematical programming methods, users input system information such as load profiles, energy/demand charge rates, and battery characteristics to construct a standard programming problem, which typically involves a large number of constraints and decision variables. The problems are then solved by optimization solvers to obtain numerical solutions. Such kind of methods cannot directly link the obtained optimal battery sizes to input parameters and requires case-by-case analysis. In this paper, we present an objective quantitative analysis of costs and benefits for customer-side BSS, and thereby identify key factors that affect optimal sizing. We then develop simple but effective guidelines for determining the most cost-effective battery size. The proposed analytical sizing methods are innovative, and provide engineering insights on how the optimal battery size varies with system characteristics. We illustrate the proposed methods using practical building load profile and utility rate. The obtained results are compared with the ones using mathematical programming based methods for validation.

A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings

This paper presents a variation focused cluster analysis strategy to identify typical daily heating energy usage profiles of higher education buildings. Different from the existing cluster analysis studies which were primarily developed using Euclidean distance as the dissimilarity measure and tended to group the daily load profiles with similar magnitudes, Partitioning Around Medoids (PAM) clustering algorithm with Pearson Correlation Coefficient-based dissimilarity measure was used in this study to group the daily load profiles on the basis of the variation similarity. A comparison of the proposed strategy with a k-means cluster analysis with Euclidean distance as the dissimilarity measure was also performed. The performance of the proposed strategy was tested and evaluated using the three-year hourly heating energy usage data collected from 19 higher education buildings in Norway. The results demonstrated the effectiveness of the proposed strategy in identifying the typical daily energy usage profiles. The identified typical heating load profiles provided the information such as the peaks and troughs of the daily heating demand, daily high heating demand period and daily load variation. The identified profiles also helped to categorize multiple buildings into different groups in terms of the similar energy usage behaviors to support further energy efficiency initiatives.

The generation of domestic hot water load profiles in Swiss residential buildings through statistical predictions

A long-term field study recording domestic hot water (DHW) consumption in households was used to tune a load profile generator. The methodology used in this load profile generator is also applicable to electric loads in distribution grids. Accurate DHW load profiles are essential to estimate the performance of renewable energy systems. One day and long-term randomly generated DHW profiles are useful for simulation, sizing and optimization of components in solar hot water installations such as storage tank, heat exchanger, collector area and additional heater. This work is also relevant to create standards for product testing and certification.DHW usage and draw off patterns are geographically dependent, so recent and local measurements are required to tune models and create accurate load profiles.Measurements show that DHW consumption is very volatile. The daily average value varies from 20 to 40l per person (60°C outlet temperature). The profiles underlie some trends (predominantly consumption in the morning or in the evening or spread over the day). The DHW consumption in Switzerland follows similar patterns observed in other countries, such as no significant decrease in consumption during weekends, and no strong correlation with weather conditions neither outdoor temperature nor rain.

Analysis of heating load diversity in German residential districts and implications for the application in district heating systems

In recent years, the application of district heating systems for the heat supply of residential districts has been increasing in Germany. Central supply systems can be very efficient due to diverse energy demand profiles which may lead to reduced installed equipment capacity. Load diversity in buildings has been investigated in former studies, especially for the electricity demand. However, little is known about the influence of single building characteristics (such as building envelope or hot water demand) on the overall heating peak load of a residential district. For measuring the diversity, the peak load ratio (PLR) index is used to represent the percentage reduction of peak load of a district system from a simple sum of individual peak loads of buildings. A total of 144 residential building load profiles have been created with the dynamic building simulation software IDA ICE for a theoretical analysis in which the PLR reaches 15%. Within this study, certain district features are identified which lead to higher diversity. Furthermore, these results are used in a district heating simulation model which confronts the possible advantage of reduced installed capacity with the practical disadvantage of heat distribution losses. Likewise, the influence of load density and the district́s building structure can be analyzed. This study shows that especially in districts with high load density, which consist of newly constructed buildings with low supply temperature and high influence of the hot water demand, the advantages of load diversity can be exploited.

Employing Battery Storage to Increase Photovoltaic Self-sufficiency in a Residential Building of Sweden

Photovoltaic (PV) or hybrid PV-battery systems are promising to supply power for residential buildings. In this study, the load profile of a multi apartment building in Gothenburg and the PV production profile under local weather conditions are compared and analyzed. Three different types of batteries, including lead acid, NaNiCl (Sodium-Nickel-Chloride) and Lithium ion, are studied in combination with the PV systems. It is found that Lithium ion battery system is superior in achieving higher Self-Sufficiency Ratio (SSR) with the same Life Cycle Cost (LCC). Achieving high SSR with the hybrid PV-battery system is unrealistic because of the seasonal mismatch between the load and electricity production profile. The capacity match between the PV and battery to maximize SSR was investigated, showing different trends under system LCC range of 0.1-40 Million SEK (1SEK≈0.12USD). The system LCC should be lower than 10.6 Million SEK (at the SSR of 36%) in order to keep the payback time positive.

A Modeling Framework for Developing Load Profiles in Buildings

In this paper, the adapted time-series regression (ATSR) model is used for developing appliance energy usage profiles for a building which utilizes meter readings, and individual appliance usage using data measurements from installed power meter respectively. For this purpose, statistical models were produced for a building as well as for individual appliances. This assists in understanding the usage patterns for all types of appliances and identifies the factors that may affect the pattern. In addition, establishing a general model for a building based on different appliance use will provide more precise data than developing a model based on total consumption for the building. This will provide an insight into the contribution of each appliance on total consumption.

Solar Assisted Ground Source Heat Pump in Cold Climates

The geothermal heat pump (or ground source heat pump) uses the ground as heat source or sink for heating and cooling respectively. The design of the borehole field is the key element of these systems since the wrong evaluation of the boreholes’ length affects the initial costs and/or the energy performance of the heat pump. The geothermal heat pumps are considered as renewable energy technologies, consequently can help the community to reduce the primary energy uses and also the CO2 emissions. However the sustainability and efficiency are ensured in the long period only when the heat balance through the ground is guaranteed.This work evaluates the thermal behavior of ground source heat pumps in cold climates, where the thermal load profile of buildings is not balanced between heating and cooling, especially in residential sector characterized by low internal loads. In these contexts, the heat pump mainly works in heating mode, extracting continuously heat from the ground. As a result, the ground temperature decreases gradually during the years affecting the energy performance of the heat pump. A possible solution to this problem is to use solar thermal collectors to stabilize or gradually increase the mean ground temperature (these systems are called Solar Assisted Ground Source Heat Pump – SAGSHP).In this work a multi floors residential building with 12 flats (88 m2 each) is analyzed in three climate zones, making use of the simulation tool TRNSYS. Different configurations of the plant system have been investigated and the case without the solar thermal collectors has been considered as reference.