Commercial Building Energy Management Research Articles

Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS.

Data Driven PMV-Comfort and Energy Consumption Control in Common Buildings

HVAC systems are essential in the energy management of commercial buildings. The main goal for HVAC system is to improve productivity of the inhabitants by providing comfortable indoor environment. The most common tool for evaluating comfort is PMV index, non-linear combination of indoor environment state variables. The Paper considers optimization of energy consumption of the building by combining state space expansion and microgrid approach.

A hybrid deep learning and clonal selection algorithm-based model for commercial building energy consumption prediction.

In contemporary society, commercial buildings, as a crucial component of urban development, face increasingly prominent energy consumption issues, posing significant challenges to the environment and sustainable development. Traditional energy management methods rely on empirical models and rule-based approaches, which suffer from low prediction accuracy and limited applicability. To address these issues, this study proposes a commercial building energy consumption prediction and energy-saving strategy model based on hybrid deep learning and optimization algorithms. This model integrates convolutional neural networks (CNN), gated recurrent units (GRU), and the clonal selection algorithm (CSA), aiming to enhance the accuracy and efficiency of energy consumption predictions. Experimental results demonstrate that the CNN-GRU-CSA Network (CGC-Net) model achieves mean absolute errors (MAE) of 17.12, 16.73, 16.62, and 15.94 on the Building Data Genome Project (BDGP), Commercial Building Energy Consumption Survey (CBECS), Nonresidential Building Energy Performance Benchmark (NEPB), and Building Energy Efficiency Benchmark (BEBDEE) datasets, respectively, significantly outperforming traditional methods and other models. Additionally, the model exhibits faster inference and training times. These results validate the stability and superiority of the CGC-Net model, providing an innovative solution and essential technical support for commercial building energy management.

A Review on Optimal Energy Management in Commercial Buildings

The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals.

Smart Energy Consumption Control in Commercial Buildings Using Machine Learning and IOT

In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.

Hierarchical Economic Model Predictive Control Approach for a Building Energy Management System With Scenario-Driven EV Charging

To handle the increasing number of electric vehicles (EVs) and their effect on the infrastructure, intelligent and coordinated charge management is necessary. In this paper, this problem is considered in the context of a commercial building energy management system (EMS) with vehicle-to-grid capable employee EV charging stations (EVCSs). We propose a hierarchical economic model predictive control (EMPC) scheme for the operation of the EMS with EV charge management. An aggregator plans the operation of the EMS and an aggregated perspective of the EVCSs. A distributor then allots the aggregated charge power to the individual EVCSs. Both layers employ EMPC to jointly consider the objectives of monetary costs, building temperature comfort, EV charge satisfaction and battery degradation. For the predictions of EV usage behavior, scenario generation based on usage data and user input is employed. The main contributions of this paper are the symmetric consideration of EV charging objectives using EMPC in both levels of the hierarchy, as well as the introduction of a scalable and adaptable averaged scenario generation approach.

Design of IoT based smart compact energy meter for monitoring and controlling the usage of energy and power quality issues with demand side management for a commercial building

Internet of Things (IoTs) plays a vital role in energy sector which introduces a smart metering and monitoring system. The smart monitoring focuses on incorporating smart meters and control techniques which requires smart equipment control, bidirectional communication, integration of network and users. In this paper, a new Commercial Building Energy Management System (CBEMS) using IoT based Smart Compact Energy Meter (SCEM) is proposed to monitor and control the energy usage and power quality issues. In addition, Demand Side Management (DSM) for a commercial building in proposed using IoT. Here, low-voltage metering hardware uses Advanced Reduced Instruction Set Computing (RISC) Machines (ARM) cortex and real-time operating system. The choice of ARM microprocessor aims at rendering a wide range of complex operations such as data acquisition, processing, disturbance detection and classification, consumption and data exchange which consequently requires optimization of the embedded software. The IoT based SCEMs with CBEMS is implemented in real time commercial building at M/s Quantanics Techserv Private Limited. Here, the load pattern of M/s Quantanics Techserv Private Limited is dynamically optimized using DSM after successfully integrating CBEMS. The operation of monitoring and controlling commercial appliances are examined and validated remotely using the mobile Blynk application. The proposed SCEMs analyzesthe power quality at low cost with high accuracy in detecting and classifying the disturbances. The proposed SCEM output values are compared with standard Elmeasure iM-8000 meter and the results are tabulated. • Designing Smart Compact Energy Meter (SCEM) with inbuilt IoT for monitoring energy consumption. • Proposing Commercial Building Energy Management System (CBEMS) for analyzing real time data. • Implementing Demand Side Management and classifying power quality disturbances. • Performance of SCEM is validated in a commercial building at M/s Quantanics Techserv Pvt. Ltd. • Recorded values of SCEM is compared with standard Elmeasure iM-8000 meter.

A Heuristic-Based Smart HVAC Energy Management Scheme for University Buildings

Energy management in commercial buildings is a challenging task due to their specific set of requirements. One such building that has not been fully investigated in the literature to provide energy efficiency is a university building. There are many challenges associated while managing the energy of a university building, such as-scheduling of classes, availability of faculty, and capacity of classrooms. To address these challenges for providing better energy efficiency, an efficient heating, ventilation, and air-conditioning (HVAC) management scheme for a university building is presented in this paper. The HVAC loads are chosen as these are more flexible in the classrooms than other loads, such as-lighting and projectors. In this paper, the HVAC energy management problem is formulated as a mixed-integer linear programming (MILP) problem. To solve this problem, a heuristic-based algorithm is proposed, which optimally minimizes the use of HVAC without affecting user comfort. Moreover, it also minimizes the cost of rescheduling the classes on a given day. The results obtained on the dataset traces taken from a university building clearly indicate that the proposed scheme reduces the energy demand of HVAC systems by 19.75% for an entire week without affecting the user comfort. Moreover, this scheme shows superior performance when compared with existing commercial demand response management schemes with respect to load reduction and cost savings.

Hybrid AC/DC Microgrid testing facility for energy management in commercial buildings

With ever increasing energy generation diversity and energy storage becoming affordable, distribution networks are becoming more complex than ever before. This complexity can be utilized to benefit the distribution networks as well as end consumers in the form of controlled Microgrids. Microgrids are not simply distributed generation and energy storage systems; solar systems and battery banks, rather a complete design of hardware and software for specific uses and users. All the different elements need to be designed to work together to provide stable, efficient and sustainable power delivery to the end user. An experimental Hybrid-Microgrid testing facility is presented in this paper that implements high-efficiency distribution architectures combining both AC and DC networks. This testing facility provides a research testbed for investigating different aspects of Microgrid systems, employing a total of 15.5 kW of reconfigurable Solar PV and 80 kWh of lithium energy storage on a 145 kVA commercial building load located at Griffith University. Implementation results along with control system simulation results are presented in this paper for distributed renewable generation, Static Synchronous Compensators, advanced control methodologies and forecasting methods for energy management purposes.

LEVERAGING ARTIFICIAL NEURAL NETWORKS FOR OPTIMIZING ENERGY CONSUMPTION IN COMMERCIAL BUILDINGS

Optimizing energy consumption in commercial buildings is becoming increasingly essential due to escalating energy costs and the demand for sustainable practices. Artificial Neural Networks (ANNs) have emerged as a powerful tool to address these challenges by offering real-time, intelligent solutions that adapt to usage patterns and environmental conditions. Inspired by the human brain, ANNs consist of layers of artificial neurons connected through weighted links. They are trained using historical and real-time data to recognize complex consumption patterns, predict future energy needs, and efficiently adjust systems like lighting, heating, ventilation, and air conditioning (HVAC). One of the primary advantages of ANNs is their ability to provide highly accurate energy demand forecasts. By analyzing extensive datasets—such as building usage, weather conditions, and occupancy levels—ANNs offer more precise predictions compared to traditional methods. This allows for proactive adjustments to HVAC systems, thereby enhancing energy efficiency and minimizing waste. ANNs also excel in adaptability, continuously learning and adjusting to new information and changing conditions, which is particularly valuable in environments with fluctuating usage and occupancy. Furthermore, integrating ANNs with Building Management Systems (BMS) adds a layer of intelligence and automation, optimizing energy consumption in real-time while considering parameters like occupancy levels and variable energy costs. This leads to substantial reductions in operational costs and energy waste. ANNs also simplify traditional control systems and enhance occupant comfort by maintaining optimal environmental conditions. However, challenges such as the need for high-quality training data, complex integration with existing systems, and data security concerns must be addressed. Despite these challenges, ANNs demonstrate significant potential for improving energy management in commercial buildings, with promising results from various studies indicating their effectiveness in reducing energy consumption and operational costs.

Energy management of commercial buildings – A case study from a POET perspective of energy efficiency

This paper aims at analyzing the energy management activities for commercial buildings of a financial service company in South Africa by energy efficiency in terms of performance, operation, equipment and technology (POET). The sustainability of a general energy management program is discussed within this POET framework. As an application of this discussion to the commercial building scenario, the award winning energy management program of this financial service group company is featured from the POET perspective of energy efficiency. The case study shows that the POET based framework can not only cover all major energy management activities, but also identify further energy efficiency improvement opportunities.

Data Analytics for Managing Power in Commercial Buildings

Commercial buildings are significant consumers of electricity. We propose a number of methods for managing power in commercial buildings. The first step toward better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this article, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power-saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semisupervised approach combining hidden Markov models (HMMs) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two-step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.

Implementation of predictive control in a commercial building energy management system using neural networks

Most existing commercial building energy management systems (BEMS) are reactive rule-based. This means that an action is produced when an event occurs. In consequence, these systems cannot predict future scenarios and anticipate events to optimize building operation. This paper presents the procedure of implementing a predictive control strategy in a commercial BEMS for boilers in buildings, and describes the results achieved. The proposed control is based on a neural network that turns on the boiler each day at the optimum time, according to the surrounding environment, to achieve thermal comfort levels at the beginning of the working day. The control strategy presented in this paper is compared with the current control strategy implemented in BEMS that is based on scheduled on/off control. The control strategy was tested during one heating season and a set of key performance indicators were used to assess the benefits of the proposed control strategy. The results showed that the implementation of predictive control in a BEMS for building boilers can reduce the energy required to heat the building by around 20% without compromising the user’s comfort.

Energy consumption quota management of Wanda commercial buildings in China

There is limited research of commercial buildings’ energy use data conducted based on practical analysis in China nowadays. Some energy consumption quota tools like Energy Star in U.S or VDI 3807 in Germany have limitation in China's building sector. This study introduces an innovative methodology of applying energy use quota model and empirical management to commercial buildings, which was in accordance of more than one hundred opened shopping centers of a real estate group in China. On the basis of statistical benchmarking, a new concept of “Modified coefficient”, which considers weather, occupancy, business layout, operation schedule and HVAC efficiency, is originally introduced in this paper. Our study shows that the average energy use quota increases from north to south. The average energy use quota of sample buildings is 159 kWh/(m2.a) of severe cold climate zone, 179 kWh/(m2.a) of cold zone, 188 kWh/(m2.a) of hot summer and cold winter zone, and 200 kWh/(m2.a) of hot summer and warm winter zone. The energy use quota model has been validated in the property management for year 2016, providing a new method of commercial building energy management to the industry. As a key result, there is 180 million energy saving potential based on energy quota management in 2016, equals to 6.2% saving rate of actual energy use in 2015.

Optimisation of energy management in commercial buildings with weather forecasting inputs: A review

Information about the patterns that govern the energy demand and onsite generation can generate significant savings in the range of 15–30% in most cases and thus is essential for the management of commercial building energy systems. Predominantly, heating and cooling in a building as well as the availability of solar and wind energy are directly affected by variables such as temperature, humidity and solar radiation. This makes energy management decision making and planning sensitive to the prevalent and future weather conditions. Research attempts are being made using a variety of statistical or physical algorithms to predict the evolution of the building load or generation in order to optimise the building energy management. The response of the building energy system to changes in weather conditions is inherently challenging to predict; nevertheless numerous methods in the literature describe and utilise weather predictions. Such methods are being reviewed in this study and their strengths, weaknesses and applications in commercial buildings at different prediction horizons are discussed. Furthermore, the importance of considering weather forecasting inputs in energy management systems is established by highlighting the dependencies of various building components on weather conditions. The issues of the difficulty in implementation of integrated weather forecasts at commercial building level and the potential added value through energy management optimisation are also addressed. Finally, a novel framework is proposed that utilises a range of weather variable predictions in order to optimise certain commercial building systems.

Indoor Human Thermal Comfort Optimal Control with Desiccant Wheel Cooling System

Human thermal comfort is an important concern in the energy management of commercial buildings. Human thermal comfort research focuses mostly on the temperature control or the humidity control while based on human thermal comfort index control is ignored. In this paper, an optimal human thermal comfort control model (OHTCM) for a desiccant wheel cooling system is presented for the dehumidification and cooling of a commercial building in summer seasons. The OHTCM has two objectives. The first objective of the OHTCM is to minimize the predicted percentage of dissatisfied (PPD) which is the human thermal comfort index, and the second objective is to minimize the power consumption of the desiccant wheel cooling system. Model predictive control (MPC) strategy has the ability to handle constraints, being able to use simple models and to change controls dynamically in terms of temperature, humidity and air velocity changes, which makes it very practical to use in indoor human thermal comfort control problem. Therefore, MPC strategy is applied to implement the optimal operation of the desiccant wheel cooling system during working hours of the commercial building. To illustrate the practical applications of the MPC strategy, the optimization of the desiccant wheel cooling system in a commercial building of South Africa is studied.

96/06834 Learning from experiences with energy management of commercial buildings

96/06834 Learning from experiences with energy management of commercial buildings

Energy Management in Commercial Buildings

The construction industry is a great consumer of energy and other resources. Commercial buildings consume nearly 15% of all the energy used in the United States annually. Commercial and residential buildings, taken together, annually consume just over one-third of the nation's energy pie. The age of cheap fossil-fueled energy is past. Skyrocketing energy costs and the growing scarcity of other resources has made energy conservation a fact of life for the entire construction industry. Everyone involved in the industry — architects, engineers, contractors, building suppliers and equipment manufacturers — must now carry on their business with a careful eye on energy consumption. The construction industry can help reduce our reliance on foreign energy products by designing and operating buildings in a more energy-efficient manner.