Load reduction server and system using HVAC integrated control method

Abstract

This load reduction system using an HVAC integrated control method comprises a cloud-based HVAC-energy management system (EMS) server that collects monitoring data for a cooling and heating air conditioning facility for each region and each building and reduces air conditioning energy consumption, wherein the HVAC-EMS server includes a memory for storing at least one instruction and a processor. The at least one instruction is executed by the processor to cause the HVAC-EMS server to: provide an automated operation process, suitable for a building energy environment, of a cooling and heating system; perform data acquisition and facility control through connection with an IoT solution; perform energy efficiency analysis of the cooling and heating air conditioning facility for each region and each building; and search for an improvement process according to the efficiency analysis performance result.

Description

Load reduction servers and systems using HVAC integrated control methods

[0001] The present disclosure relates to a load reduction server and system using an HVAC integrated control method, and more particularly, to a server and system providing a building HVAC comprehensive energy management system comprising an artificial intelligence analysis technology-based monitoring system, diagnostic service, control and management solution.

[0002] Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application, and their inclusion in this section is not intended to be admitted as prior art.

[0003] HVAC, short for "Heating, Ventilation, and Air Conditioning," is a system and technology for controlling the temperature and atmospheric conditions inside a building. HVAC systems are primarily used to regulate the temperature inside a building and circulate air to maintain a comfortable indoor environment. They raise the temperature to keep indoor spaces warm during cold weather or in the winter. Heating is typically accomplished using boilers, gas heaters, or heat pumps. They also replace indoor air with outdoor air, improving indoor air quality. Air conditioners and cooling towers are used to cool indoor spaces during hot weather or in the summer by supplying fresh air and expelling polluted air, ensuring air circulation.

[0004] HVAC systems play a crucial role in controlling temperature and humidity within a building, providing a comfortable environment, maintaining air quality, and improving energy efficiency. Depending on the building type and size, various types of HVAC systems are used, providing building owners and users with a comfortable environment and energy-saving opportunities.

[0005] Meanwhile, Vietnam, an emerging developing country with an annual high temperature and humidity climate, has a high demand for cooling energy. Due to rising gross domestic product (GDP) and improving living conditions, the spread of air conditioning and demand for cooling are rapidly increasing. In Vietnam, cooling accounts for the largest share of electricity consumption in typical office buildings, at 47.8%. Air conditioning energy consumption in the average Vietnamese household ranges from a minimum of 25% to a maximum of 50%, significantly higher than the average domestic air-conditioned household's energy consumption of 8%. Furthermore, in southern Vietnam, the percentage of air-conditioning users who keep their devices on until 5:00 AM reaches 50%.

[0006] Vietnam, too, is pursuing net-zero emissions by 2050, a goal it aims to achieve globally in carbon reduction efforts. However, the widespread adoption of energy management systems for buildings, homes, and commercial facilities remains underdeveloped, and awareness of the potential for energy savings through the management of building energy systems remains low. Furthermore, Vietnam faces high energy waste due to a lack of public awareness of energy efficiency, energy-saving methods, and effective consumption patterns linked to electricity tariff systems.

[0007] Vietnam, a hub for Asian manufacturing, has seen its electricity consumption more than triple over the past 12 years. However, electricity rates are applied differently depending on the time of day, causing difficulties for citizens and creating numerous problems for the industry, which urgently need to be resolved.

[0008] The load reduction server and system using the HVAC integrated control method according to the embodiment apply the HVAC-EMS platform technology development system, an energy management platform that can reduce energy waste and optimize the power usage of a building in accordance with the time-based power usage fee differential policy.

[0009] Furthermore, through examples, the project provides an Integrated Building Automation System (BAS), an IoT-based building automation control solution, enabling efficient, integrated operation and management of key building facilities and systems. Furthermore, through examples, the project provides an AI-based energy analysis service and analyzes HVAC operation scheduling based on AI energy demand forecasting.

[0010] In addition, through examples, precise energy savings are realized through intelligent control based on energy data, and a 3D-based visualization monitoring service is provided.

[0011] Additionally, it provides real-time energy efficiency monitoring information in 3D-based form through examples, enabling flexible expansion of various linked products.

[0012] Furthermore, through examples, an open platform tailored to each building's specific needs can be provided. Furthermore, the building heating and cooling energy management platform offers a lineup of integrated facility controls for optimized air conditioning and enables flexible expansion of various linked products.

[0013] In addition, the embodiment distinguishes between individual air conditioning products and central air conditioning products, and enables heating and cooling control of individual air conditioning products and central air conditioning products.

[0014] In addition, the embodiment allows for flexible linkage of related products for each purpose of heating and cooling, such as security, network, infrastructure, equipment, and environmental energy.

[0015] In addition, through examples, it is possible to carry out global standardization jointly conducted internationally to implement IoT and artificial intelligence control of digital twin-type buildings.

[0016] In addition, through examples, the standardization can be expanded to include commercial complexes, hotel complexes, residential areas, wholesale and retail stores, offices, schools, and other scalable forms.

[0017] However, the problems to be solved according to one embodiment are not limited to those mentioned above.

[0018] A load reduction system using an HVAC integrated control method according to an embodiment comprises a cloud-based HVAC-EMS (Energy Management System) server that collects monitoring data of heating, cooling, and air conditioning equipment by region and building to reduce air conditioning energy consumption; wherein the HVAC-EMS server comprises a memory that stores at least one command; and a processor, wherein at least one command is executed by the processor, thereby providing an automated operation process of a heating, cooling, and air conditioning system suitable for a building energy environment, performing data acquisition and equipment control through linkage with an IoT solution, performing energy efficiency analysis of heating, cooling, and air conditioning equipment by region and building, and exploring an improvement process based on the results of the efficiency analysis.

[0019] In addition, the HVAC-EMS server can perform product-by-product operation status analysis and usage analysis and reporting of heating and cooling equipment, perform energy efficiency analysis of buildings and heating and cooling equipment, and analyze the building's overall load usage pattern to derive an optimal operation algorithm.

[0020] Load reduction servers and systems utilizing the HVAC integrated control method described above enable efficient, integrated management of various facilities for a comfortable indoor environment and efficient, optimized energy use. Furthermore, they generate cost savings related to energy use.

[0021] In addition, through examples, we will promote the global expansion of excellent companies with domestic technology development potential through international joint technology development projects, secure domestic and international demand, and promote energy efficiency.

[0022] In addition, the development of air conditioning management system technology for energy efficiency in each building through examples enables the development of technology that combines Southeast Asian heating and cooling air conditioning management system products with domestic energy efficiency technology, starting with the Samsung Vietnam Industrial Complex.

[0023] Furthermore, based on the data collected through the examples, it will be possible to develop energy solutions using artificial intelligence, analyze big data, expand business to cloud platforms, and discover diverse demand sources.

[0024] Furthermore, through examples, the current state of the Vietnamese electricity market can be categorized into two types: preemptive prevention and standby power reduction. Analysis of the necessary technologies and applications for each type will be conducted. Furthermore, through examples, the company plans to leverage local Vietnamese networks and certified systems, enabling rapid commercialization in the future.

[0025] Furthermore, as Vietnam aims to reduce national energy consumption, a key issue in its energy security, the initiative will enable rapid technology diffusion across the country in a way that maximizes energy efficiency relative to power output.

[0026] The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the detailed description of the present invention or the composition of the invention described in the claims.

[0027] FIG. 1 is a drawing for explaining an HVAC-EMS (Heating, Ventilation, and Air Conditioning - Energy Management System) according to an embodiment.

[0028] FIG. 2 is a drawing showing a load reduction system using an HVAC integrated control method according to an embodiment.

[0029] Figure 3 is a block diagram showing an HVAC-EMS server according to an embodiment.

[0030] Figure 4 is a diagram showing the configuration of a command set stored in memory according to an embodiment.

[0031] Figure 5 is a diagram showing an example of a model that learns data stored in a learning unit according to an embodiment.

[0032] Figure 6 is a drawing showing the visualization result by the visualization model according to the embodiment.

[0033] Figure 7 is a diagram showing a business model of a load reduction system using an HVAC integrated control method according to an embodiment.

[0034] Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the drawing numbers, identical or similar components will be given the same reference numbers, and redundant descriptions thereof will be omitted. The suffixes "module" and "part" used for components in the following description are assigned or used interchangeably only for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. In addition, when describing the embodiments disclosed in this specification, if it is determined that a specific description of a related known technology may obscure the gist of the embodiments disclosed in this specification, a detailed description thereof will be omitted. In addition, the attached drawings are only intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and technical scope of the present invention.

[0035] Terms that include ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another.

[0036] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0037] In this application, terms such as “include” or “have” are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but should be understood not to exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0038] In this specification, the term "unit" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Furthermore, a single unit may be realized using two or more pieces of hardware, and two or more units may be realized by a single piece of hardware.

[0039] Some of the operations or functions described herein as being performed by a terminal, apparatus, or device may instead be performed by a server connected to the terminal, apparatus, or device. Similarly, some of the operations or functions described herein as being performed by a server may also be performed by a terminal, apparatus, or device connected to the server.

[0040] Hereinafter, the present invention will be described in detail with reference to the attached drawings.

[0041] FIG. 1 is a drawing for explaining an HVAC-EMS (Heating, Ventilation, and Air Conditioning - Energy Management System) according to an embodiment.

[0042] Referring to Figure 1, the HVAC-EMS according to the embodiment applies the HVAC-EMS platform technology development system, an energy management platform that reduces energy waste and optimizes a building's power usage in accordance with a time-based differential electricity usage rate policy. Furthermore, it provides an integrated BAS (Building Automation System), an IoT-based building automation solution, and efficiently manages the integrated operation of key facilities and systems within the building.

[0043] Furthermore, through examples, AI-based energy analysis services and AI-based energy demand forecasting-based HVAC operation scheduling analysis are performed. The HVAC-EMS implements precise energy savings through energy data-driven intelligent control and provides 3D-based visualized monitoring services. Furthermore, it provides real-time, 3D-based, three-dimensional energy efficiency-related monitoring information. This enables flexible expansion of various linked products and provides an open platform tailored to each building's needs.

[0044] Furthermore, the building heating and cooling energy management platform provides a lineup of optimized, integrated facility controls for air conditioning and enables flexible expansion of various linked products. Furthermore, the platform distinguishes between individual and central air conditioning products and enables heating and cooling control for both. Furthermore, the platform enables flexible linking of linked products for specific heating and cooling purposes, such as security, networking, infrastructure, equipment, and environmental energy. Furthermore, the platform facilitates international standardization for IoT and AI-based control of digital twin-type buildings.

[0045] FIG. 2 is a diagram illustrating a load reduction system using an HVAC integrated control method according to an embodiment.

[0046] Referring to FIG. 2, a load reduction system using an HVAC integrated control method according to an embodiment may be configured to include a cloud-based HVAC-EMS (Energy Management System) server (100) that collects monitoring data of heating, cooling, and air conditioning equipment by region and building to reduce air conditioning energy consumption.

[0047] In the embodiment, the HVAC-EMS server (100) provides an automated operation process of a heating and cooling system suitable for the building energy environment, acquires data and controls equipment through linkage with an IoT solution, performs energy efficiency analysis of heating and cooling equipment by region and building, and explores an improvement process based on the results of the efficiency analysis.

[0048] In addition, the HVAC-EMS server (100) performs analysis and reporting of the operating status and usage of each product of heating and cooling equipment, performs energy efficiency analysis of buildings and heating and cooling equipment, and analyzes the building's overall load usage pattern to derive an optimal operation algorithm.

[0049] FIG. 3 is a block diagram showing an HVAC-EMS server according to an embodiment.

[0050] In this embodiment, a server is a computing system that provides services to other computers or devices on a computer network or stores and manages data. The HVAC-EMS server (100) accepts requests from other computers or devices, called clients, and provides responses or data in response to those requests. The server (100) configuration illustrated in FIG. 3 is merely a simplified example.

[0051] The communication unit (110) may be configured regardless of the communication mode, such as wired or wireless, and may be configured with various communication networks, such as a personal area network (PAN) and a wide area network (WAN). In addition, the communication unit (110) may operate based on the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth. For example, the communication unit (110) may be responsible for transmitting and receiving data required to perform a technique according to an embodiment of the present disclosure.

[0052] The memory (120) may refer to any type of storage medium. For example, the memory (120) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk. Such a memory (120) may also constitute a database as illustrated in FIG. 1.

[0053] The memory (120) can store at least one instruction that can be executed by the processor (130). In addition, the memory (120) can store any type of information generated or determined by the processor (130) and any type of information received by the server (200). For example, the memory (120) stores RM data and RM protocols according to the user, as will be described later. In addition, the memory (120) stores various types of modules, instruction sets, and models.

[0054] The processor (130) may perform technical features according to embodiments of the present disclosure, which will be described later, by executing at least one instruction stored in the memory (120). In one embodiment, the processor (130) may be configured with at least one core and may include a processor for data analysis and / or processing, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU) of a computer device.

[0055] FIG. 4 is a diagram showing the configuration of a command set stored in memory according to an embodiment.

[0056] Referring to FIG. 4, the instruction set according to the embodiment may be configured to include a collection unit (121), a preprocessing unit (122), a learning unit (123), an analysis unit (124), and a feedback unit (125). The term 'unit' used in this specification should be interpreted to include software, hardware, or a combination thereof, depending on the context in which the term is used. For example, the software may be machine language, firmware, embedded code, and application software. As another example, the hardware may be a circuit, a processor, a computer, an integrated circuit, an integrated circuit core, a sensor, a MEMS (Micro-Electro-Mechanical System), a passive device, or a combination thereof.

[0057] The collection unit (121) collects energy usage data and energy monitoring data for each building. In addition, it collects a training data set for an artificial neural network model used for data processing in the HVAC-EMS server.

[0058] The preprocessing unit (122) preprocesses the collected training data to remove biased or discriminatory data from the collected artificial intelligence learning data set. In an embodiment, the preprocessing unit (122) preprocesses the collected training data set and processes it into a form suitable for artificial intelligence model learning. For example, the preprocessing unit (122) may perform processes such as noise removal, outlier removal, and missing value processing. In addition, the preprocessing unit (122) may normalize data, remove outliers, or adjust the scale of data through data preprocessing to prevent the model from learning unnecessary patterns.

[0059] The learning unit (123) trains a deep learning neural network with a collected training data set to implement a deep learning model. Fig. 5 is a diagram illustrating an example of a model stored in the learning unit and learning data according to an embodiment. Referring to Fig. 5, an analysis model (1), a generation model (2), and a visualization model (3) may be stored in the learning unit (123) of the memory. Each module or model may be in the form of an application executable by the processor (130).

[0060] The analysis model (1) is an artificial neural network model that analyzes monitoring data for each building. In an embodiment, the analysis model (1) analyzes monitoring data for each building by time or analyzes change trends so that an optimal HVAC-EMS operation plan can be derived based on the analysis results. The generation model (2) is an artificial neural network model that performs deep learning-based HVAC scheduling and optimal energy control through energy data pattern clustering and deep learning-based facility demand forecasting. The visualization model (3) is an artificial neural network model that visualizes energy control results and air conditioning results for each building. Fig. 6 is a drawing showing a visualization result by a visualization model according to an embodiment. Referring to Fig. 6, the visualization model in an embodiment visualizes and outputs load forecast for each facility, usage analysis results, operation scheduling analysis results, and energy saving effect analysis results in the form of graphs, etc.

[0061] A model in this specification may refer to any form of computer program that operates based on a network function, an artificial neural network, and / or a neural network. Throughout this specification, the terms model, neural network, network function, and neural network may be used interchangeably. A neural network is a network in which one or more nodes are interconnected through one or more links to form input node and output node relationships within the neural network. The characteristics of a neural network can be determined based on the number of nodes and links within the neural network, the correlation between the nodes and links, and the weight value assigned to each link. A neural network may be composed of a set of one or more nodes. A subset of the nodes constituting the neural network may constitute a layer.

[0062] A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to an input layer and an output layer. A deep neural network may include a convolutional neural network (CNN), a recurrent neural network (RNN), an autoencoder, a generative adversarial network (GAN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a Q network, a U network, a Siamese network, a generative adversarial network (GAN), a transformer, and the like. The description of the above-described deep neural network is merely an example, and the present disclosure is not limited thereto.

[0063] Neural networks can learn through at least one of the following methods: supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, or reinforcement learning. Neural network learning can be the process of applying knowledge to the neural network to perform a specific action.

[0064] In one embodiment, the model may include, but is not limited to, at least one of a Recurrent Neural Network (RNN), a Long Short Term Memory (LSTM) network, a Deep Neural Network (DNN), a Convolutional Neural Network (CNN), and a Bidirectional Recurrent Deep Neural Network (BRDNN).

[0065] In one embodiment, the model may be a model trained using transfer learning. Transfer learning, in this context, refers to a learning method that pre-trains a large amount of unlabeled training data using semi-supervised or self-learning methods to obtain a pre-trained model for a first task, then fine-tunes the pre-trained model to suit a second task, and trains it on labeled training data using supervised learning to implement a target model.

[0066] The feedback unit (125) evaluates the learned artificial neural network model and deep learning model. In an embodiment, the feedback unit (125) can evaluate the artificial neural network model through at least one of accuracy, precision, and recall. Accuracy is an index that measures how well the results predicted by the artificial neural network model match the actual results. Precision is an index that measures the ratio of actual positives among the results predicted as positive. Recall is an index that measures the ratio of actual positives predicted by the model as positives. In an embodiment, the feedback unit (125) can calculate the accuracy, precision, and recall of the artificial neural network model, and evaluate the artificial neural network model based on at least one of the calculated indexes.

[0067] In an embodiment, the feedback unit (125) can measure the accuracy of the artificial neural network model using an evaluation dataset. The evaluation dataset consists of data that the model did not use for training and is used to objectively evaluate the model's performance. In an embodiment, the feedback unit (125) executes the artificial neural network model using the evaluation dataset and compares the predicted value of the artificial neural network model for each input data with the actual correct answer value of the corresponding data. Thereafter, the accuracy of the model's predictions can be measured based on the comparison results. For example, the accuracy in the feedback unit (125) can be calculated as the ratio of data correctly predicted by the model among the entire data.

[0068] In addition, the feedback unit (125) can calculate the F1 score, which is an index indicating the balance of precision and recall, which is an index calculated as the harmonic mean of precision and recall, evaluate the artificial neural network model based on the calculated F1 score, generate an AUC-ROC curve, which is an index that visualizes the performance of the classification model in a graph, and evaluate the artificial neural network model based on the generated AUC-ROC curve. In an embodiment, the feedback unit (125) can evaluate that the performance of the model is better as the area under the ROC curve (AUC) is closer to 1.

[0069] In addition, the feedback unit (125) can evaluate the interpretability of the artificial neural network model. In an embodiment, the feedback unit (125) evaluates the interpretability of the artificial neural network model through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methods. SHAP (SHapley Additive exPlanations) is a library that provides an interpretation of the results predicted by the model, and the feedback unit (125) extracts SHAP values ​​from the library. In an embodiment, the feedback unit (125) can predict how much the characteristic information input to the model influenced the model prediction through the extraction of SHAP values.

[0070] Fig. 7 is a diagram showing a business model of a load reduction system using an HVAC integrated control method according to an embodiment.

[0071] Referring to Figure 7, the embodiment recruits customers for a load reduction system using an HVAC integrated control method, supplies HVAC, and collects regular subscription and membership fees from the customers. The embodiment provides HVAC optimization and energy-saving services, and distributes revenue from subscription and membership fees.

[0072] Load reduction servers and systems utilizing the HVAC integrated control method described above enable efficient, integrated management of various facilities for a comfortable indoor environment and efficient, optimized energy use. Furthermore, they generate cost savings related to energy use.

[0073] In addition, through examples, we will promote the global expansion of excellent companies with domestic technology development potential through international joint technology development projects, secure domestic and international demand, and promote energy efficiency.

[0074] In addition, the development of air conditioning management system technology for energy efficiency in each building through examples enables the development of technology that combines Southeast Asian heating and cooling air conditioning management system products with domestic energy efficiency technology, starting with the Samsung Vietnam Industrial Complex.

[0075] Furthermore, based on the data collected through the examples, it will be possible to develop energy solutions using artificial intelligence, analyze big data, expand business to cloud platforms, and discover diverse demand sources.

[0076] Furthermore, through examples, the current state of the Vietnamese electricity market can be categorized into two types: preemptive prevention and standby power reduction. Analysis of the necessary technologies and applications for each type will be conducted. Furthermore, through examples, the company plans to leverage local Vietnamese networks and certified systems, enabling rapid commercialization in the future.

[0077] Furthermore, as Vietnam aims to reduce national energy consumption, a key issue in its energy security, the initiative will enable rapid technology diffusion across the country in a way that maximizes energy efficiency relative to power output.

[0078] The disclosed content is merely an example, and various modifications and implementations can be made by a person skilled in the art without departing from the gist of the claims claimed in the patent, so the scope of protection of the disclosed content is not limited to the specific embodiments described above.

Claims

1. In a load reduction system using an HVAC integrated control method, Includes a cloud-based HVAC-EMS (Energy Management System) server that collects monitoring data on heating and cooling equipment by region and building to reduce air conditioning energy consumption; The HVAC-EMS server comprises a memory storing at least one command; and a processor, By executing at least one instruction of the above by the processor, A load reduction system using an HVAC integrated control method characterized by providing an automated operation process for a heating and cooling system suited to the building energy environment, performing data acquisition and facility control through linkage with IoT solutions, performing energy efficiency analysis of heating and cooling air conditioning facilities by region and building, and exploring improvement processes based on the results of the efficiency analysis.

2. In the first paragraph, the HVAC-EMS server; A load reduction system using an HVAC integrated control method characterized by performing product-by-product operation status analysis and usage analysis and reporting of heating and cooling equipment, performing energy efficiency analysis of buildings and heating and cooling equipment, and deriving an optimal operation algorithm by analyzing the building's overall load usage pattern.

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