A subway station ventilation and air conditioning energy-saving system and method based on load forecasting

Abstract

The present invention discloses a subway station ventilation and air conditioning energy-saving system based on load prediction, including a central control unit and an electric energy monitoring unit, a temperature and humidity monitoring unit, a flow monitoring unit, a wind speed monitoring unit, a passenger flow monitoring unit, a chiller unit, an air handling unit unit, a cooling tower unit, a water pump unit and a fan unit respectively connected and communicating with the central control unit. The present invention uses an intelligent algorithm to predict the load change of the subway station under different operating conditions, and according to the optimization algorithm, the optimal operating parameters are obtained with the optimization goal of avoiding #imgabs0# loss, and through the fuzzy algorithm, the control command is output to each unit of the ventilation and air conditioning system to adjust the relevant parameters in real time and dynamically, so as to achieve the effects of trend prediction, intelligent adjustment and control, energy-saving management, reduced energy consumption, and reduced operating costs.

Description

Technical Field

[0001] The present invention relates to the field of load forecasting and automatic control, and in particular to a subway station ventilation and air-conditioning energy-saving system and method based on load forecasting. Background Art

[0002] In recent years, with the emergence of subways, it not only brings convenience to people's travel, but also relieves traffic pressure. Subways have become an essential product in the continuous development of large cities. At present, there are still many cities in my country that are under construction or expansion. However, for the entire subway system, it is also a huge energy consumer. Since subways are usually built underground and provide better services to passengers, the requirements for subways in the operation process are relatively high, requiring a large number of high-precision instruments and equipment to ensure the safety of passengers and provide passengers with a comfortable riding environment. In this process, the power consumption of the subway is huge. Among them, the ventilation and air-conditioning system of subway stations accounts for nearly 1 / 3 of the energy consumption, so how to effectively reduce the energy consumption of the ventilation and air-conditioning system of subway stations becomes particularly important.

[0003] At present, the ventilation and air-conditioning systems of subway stations are divided into large systems and small systems according to the public areas and equipment areas. In many stations, large and small systems are provided with cooling capacity by common chillers. The large system is generally turned on during the daytime operation period, while the small system is turned on 24 hours a day. When the temperature outside the station is lower than a certain set temperature, the heat dissipation of the station is mainly achieved through ventilation, and when the temperature outside the station is higher than a certain set temperature, air conditioning is mainly used for cooling. At present, the ventilation and air-conditioning system of subway stations is designed according to the long-term maximum load and has a certain surplus capacity, but the system load shows obvious peak and valley differences with changes in passenger flow, temperature and humidity outside the station, and train numbers. The full load operation time is very short, and the load fluctuates greatly. In addition, the ventilation and air-conditioning system of subway stations also has the characteristics of large time lag.

[0004] Therefore, it is necessary to improve the current operation mode of subway station ventilation and air-conditioning systems to reduce operating energy consumption. Summary of the invention

[0005] In view of the above-mentioned problems, the purpose of the present invention is to provide a subway station ventilation and air-conditioning energy-saving system and method based on load forecasting, which combines the forecast with online monitoring, optimizes the control of the operating parameters of the ventilation and air-conditioning system, and reduces the energy consumption of the subway station ventilation and air-conditioning system while ensuring the comfort requirements of passengers.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A subway station ventilation and air conditioning energy-saving system based on load forecasting, comprising:

[0008] The power monitoring unit is used to collect the real-time power parameters of the station's electrical equipment and upload them to the central control unit;

[0009] Temperature and humidity monitoring unit, used to collect real-time temperature and humidity parameters inside and outside the station and the ventilation and air-conditioning system, and upload them to the central control unit;

[0010] Passenger flow monitoring unit, used to collect passenger flow parameters entering and leaving the station and upload them to the central control unit;

[0011] Wind speed monitoring unit, used to collect wind speeds of mechanical fresh air, supply air and return exhaust air, and upload them to the central control unit;

[0012] Flow monitoring unit, used to collect the flow of chilled water and cooling water and upload it to the central control unit;

[0013] The central control unit calculates the load forecast value of the station through intelligent algorithms based on real-time power parameters, real-time temperature and humidity parameters, passenger flow parameters, mechanical fresh air volume, and train timetable. The optimization algorithm with loss as the optimization target obtains the optimal values ​​of the chilled water supply temperature, the air supply temperature of the air handling unit, and the cooling water supply temperature, and then uses the fuzzy algorithm to output control commands to the chiller unit, the air handling unit unit, the cooling tower unit, the water pump unit, and the fan unit;

[0014] The chiller unit is used to execute the control command of the central control unit and adjust the chilled water supply temperature to the optimal value;

[0015] The air handling unit is used to execute the control command of the central control unit and adjust the air supply temperature of the air handling unit to the optimal value;

[0016] The cooling tower unit is used to execute the control command of the central control unit and adjust the cooling water supply temperature to the optimal value;

[0017] The water pump unit is used to execute the control command of the central control unit and adjust the flow of cooling water and chilled water;

[0018] The fan unit is used to execute the control commands of the central control unit and adjust the air volume of fresh air, supply air, return air and exhaust air.

[0019] As an improvement of the present invention, the electric energy monitoring unit includes a current transformer and a multifunctional electricity meter.

[0020] As an improvement of the present invention, the temperature and humidity monitoring unit includes an off-site temperature and humidity sensor arranged at the inlet of the fresh air duct, a return air temperature and humidity sensor arranged in the return air duct, a supply air temperature and humidity sensor arranged behind the air handling unit, a chilled water temperature sensor arranged at the inlet and outlet of the chiller, a cooling water temperature sensor arranged at the inlet and outlet of the cooling tower, and a wet-bulb temperature sensor arranged at the inlet of the cooling tower fan.

[0021] As an improvement of the present invention, the intelligent algorithm includes cascade, BP, SVR and other intelligent algorithms.

[0022] As an improvement of the present invention, it also includes a meteorological correction unit, which corrects the meteorological data provided by the Meteorological Bureau according to the monitored temperature and humidity outside the station, so as to use the meteorological data provided by the Meteorological Bureau to predict the temperature and humidity outside the station in the next hour or the next day, and upload it to the central control unit.

[0023] A subway station ventilation and air conditioning energy saving method based on load forecasting comprises the following steps:

[0024] Step 1: Collect the real-time power parameters of the station's electrical equipment and upload them to the central control unit;

[0025] Step 2: Collect the temperature and humidity outside the station, the temperature and humidity inside the station, the air supply temperature and humidity of the air handling unit, the supply and return water temperature of the chilled water, the supply and return water temperature of the cooling water, and the wet bulb temperature of the cooling tower inlet air, and upload them to the central control unit;

[0026] Step 3: Collect the wind speed of mechanical fresh air, air handling unit supply air, and air conditioning system return and exhaust air, and upload them to the central control unit;

[0027] Step 4: Collect the flow of chilled water and cooling water and upload them to the central control unit;

[0028] Step 5: Collect real-time passenger flow and forecast hourly passenger flow in combination with train timetable and historical passenger flow data;

[0029] Step 6: Use the meteorological correction unit to correct the meteorological data provided by the meteorological station and predict the temperature and humidity outside the station in the next time period;

[0030] Step 7, the central control unit uses the real-time power parameters obtained through monitoring, the predicted temperature and humidity outside the station, the set temperature and humidity inside the station, the predicted hourly passenger flow, the mechanical fresh air volume, and the train timetable to calculate the station load prediction value through an intelligent algorithm;

[0031] Step 8: After obtaining the station load forecast value, combine the real-time temperature and humidity parameters, wind speed and flow rate to avoid The optimization algorithm with loss as the optimization target obtains the optimal values ​​of the chilled water supply temperature, the air supply temperature of the air handling unit, and the cooling water supply temperature;

[0032] Step 9: After obtaining the optimal parameters, the fuzzy algorithm is used to output control commands to the ventilation and air-conditioning system to adjust the temperature and humidity in the station.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention uses an intelligent algorithm to predict the load changes of subway stations under different operating conditions based on a continuously updated load database, and uses an optimization algorithm to avoid The loss is the best operating parameters obtained for the optimization target. Through the fuzzy algorithm, the control commands are output to each unit of the ventilation and air-conditioning system to make real-time dynamic adjustments to the relevant parameters, so as to achieve the effects of trend prediction, intelligent adjustment and control, energy-saving management, reduced energy consumption, and reduced operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a control principle diagram of the subway station ventilation and air conditioning energy-saving system based on load forecasting of the present invention. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, a subway station ventilation and air-conditioning energy-saving system based on load forecasting of the present invention includes a central control unit and an electric energy monitoring unit, a temperature and humidity monitoring unit, a flow monitoring unit, a wind speed monitoring unit, a passenger flow monitoring unit, a meteorological correction unit, a chiller unit, an air handling unit unit, a cooling tower unit, a water pump unit and a fan unit respectively connected to and communicating with the central control unit.

[0038] The power monitoring unit mainly includes current transformers, multi-function electricity meters, etc., which are connected in series to the power lines of the station's electrical equipment to obtain the power parameters of the electrical equipment in real time and upload them to the central control unit. The power parameters include but are not limited to current, voltage, power and other information. By calculating the load rate of each device, the heat generation of the equipment can be predicted. Electrical equipment mainly refers to equipment in public areas and equipment management rooms, including but not limited to elevators, ticket machines, shielding doors, civil communication equipment / power supplies, transformers, distribution boxes, control cabinets, etc. At the same time, each device can also be equipped with a temperature sensor to collect the temperature of the equipment and upload it to the central control unit.

[0039] The temperature and humidity monitoring unit includes an off-site temperature and humidity sensor at the entrance of the fresh air duct, a return air temperature and humidity sensor in the return air duct, a temperature and humidity sensor in front of the air handling unit, a supply air temperature and humidity sensor after the air handling unit, a chilled water temperature sensor at the entrance and exit of the chiller, a cooling water temperature sensor at the entrance and exit of the cooling tower, and a wet-bulb temperature sensor at the entrance of the cooling tower fan, so as to obtain real-time temperature and humidity parameters inside and outside the station, the real-time temperature and humidity of the air inside the air conditioning system, and the real-time temperature of the chilled water and cooling water, and upload them to the central control unit. In this embodiment, the temperature and humidity inside the station are replaced by the return air temperature and humidity.

[0040] Wind speed monitoring unit: wind speed monitoring points are arranged in the straight pipes with stable airflow in the fresh air duct, supply air duct, return air duct and exhaust air duct. The measured wind speed is uploaded to the central control unit, and the corresponding air volume is calculated according to the pipe size.

[0041] The flow monitoring unit has monitoring points set in the chilled water and cooling water delivery pipelines respectively, and the measured flow is uploaded to the central control unit.

[0042] The meteorological correction unit realizes the prediction of off-site meteorological data and uses the monitored off-site temperature and humidity to correct the meteorological data provided by the Meteorological Bureau. In this way, the meteorological data provided by the Meteorological Bureau can be used to predict the off-site temperature and humidity outside the station for the next hour or the next day, and then uploaded to the central control unit.

[0043] The passenger flow monitoring unit sets the collection point on the automatic ticket checking machine, counts one passenger for each ticket checking, and uploads the passenger flow data to the central control unit. The central control unit combines the train timetable and historical passenger flow data to predict the hourly passenger flow.

[0044] The central control unit is essentially a computer dedicated to industrial control. Its hardware structure is basically the same as that of a microcomputer. The software part is embedded with a load database, intelligent artificial algorithm, optimization algorithm and fuzzy algorithm. It receives monitoring data through a signal interface, and then outputs control commands to the chiller unit, cooling tower unit, air handling unit unit, water pump and fan unit after processing. The load database is used to store the collected load data of the subway station ventilation and air-conditioning system and the corresponding operating conditions, such as meteorological data, temperature and humidity in the station, passenger flow, train timetable, fresh air volume, lighting and other equipment operating loads, etc. The relevant data in this database is constantly increasing with the operation of the subway station.

[0045] The central control unit calls the historical data of the load database and the artificial intelligence algorithm, and uses the real-time power parameters obtained by power monitoring, the measured or predicted temperature and humidity outside the station, the set temperature and humidity inside the station, the predicted hourly passenger flow, the mechanical fresh air volume, and the train timetable to calculate the predicted value of the station load. The optimization algorithm with loss as the optimization target obtains the optimal chilled water supply temperature, the air supply temperature of the air handling unit, and the difference between the cooling water supply temperature and the air wet bulb temperature, and obtains the optimal values ​​of the chilled water supply temperature, the air supply temperature of the air handling unit, and the cooling water supply temperature. After obtaining the optimal parameters, the central control unit calls the fuzzy algorithm, uses the temperature and humidity monitoring data, the operating parameters of the chiller, the operating parameters of the air handling unit, the operating parameters of the cooling tower, the operating parameters of the water pump and the operating parameters of the fan as input variables, obtains the adjustment parameters of the subway station ventilation and air conditioning system operation, and sends the adjustment parameters to the execution unit. This application uses the power parameters of the equipment, the monitoring parameters of the environment, the hourly passenger flow, the fresh air volume and the train schedule as the input neurons of the intelligent algorithm, so that it can dynamically adjust the number and operation frequency of the ventilation and air conditioning system execution unit equipment according to the changing trend of the equipment heat generation and the external environment, so as to achieve the effect of reducing energy consumption and reducing operating costs.

[0046] The chiller unit, air handling unit unit, cooling tower unit, water pump unit and fan unit serve as the execution units of the ventilation and air conditioning system. They receive control instructions from the central control unit and coordinate operations to adjust the temperature and humidity in the station.

[0047] The chiller unit is used to execute the control commands of the central control unit and adjust the evaporation pressure, the number of chillers and the load rate so that the chilled water supply temperature reaches the optimal value.

[0048] The air handling unit is used to execute the control command of the central control unit and adjust the frequency of the air handling unit so that the air supply temperature of the air handling unit reaches the optimal value.

[0049] The cooling tower unit is used to execute the control command of the central control unit and adjust the frequency of the cooling tower fan to make the cooling water supply temperature reach the optimal value.

[0050] The water pump unit is used to execute the control command of the central control unit, adjust the number and frequency of the cooling water pump and the freezing water pump to control the flow of cooling water and freezing water. Preferably, the number of chillers and freezing water pumps is the same.

[0051] The fan unit is used to execute the control commands of the central control unit and adjust the frequency of the fresh air fan, supply air fan and return and exhaust air fan to control the air volume of fresh air, supply air, return air and exhaust air.

[0052] The adjustment steps of the subway station ventilation and air conditioning energy-saving system based on load forecasting of the present invention are as follows:

[0053] Step 1: Collect the real-time power parameters of the station's electrical equipment and upload them to the central control unit;

[0054] Step 2: Collect the temperature and humidity outside the station, the temperature and humidity inside the station, the air supply temperature and humidity of the air handling unit, the supply and return water temperature of the chilled water, the supply and return water temperature of the cooling water, and the wet bulb temperature of the cooling tower inlet air, and upload them to the central control unit;

[0055] Step 3: Collect the wind speed of mechanical fresh air, air handling unit supply air, and air conditioning system return and exhaust air, calculate the corresponding flow rate, and upload it to the central control unit;

[0056] Step 4: Collect the flow of chilled water and cooling water and upload them to the central control unit;

[0057] Step 5: Collect real-time passenger flow and forecast hourly passenger flow in combination with train timetable and historical passenger flow data;

[0058] Step 6: Use the meteorological correction unit to correct the meteorological data provided by the meteorological station and predict the temperature and humidity outside the station in the next time period;

[0059] Step 7: Calculate the station load forecast value through intelligent algorithm using the real-time power parameters obtained through monitoring, the predicted temperature and humidity outside the station, the set temperature and humidity inside the station, the predicted hourly passenger flow, the mechanical fresh air volume, and the train timetable;

[0060] Step 8: After obtaining the station load forecast value, combine the real-time temperature and humidity parameters, wind speed and flow rate to avoid The optimization algorithm with loss as the optimization target obtains the optimal chilled water supply temperature, the air supply temperature of the air handling unit, and the difference between the cooling water supply temperature and the air wet bulb temperature, and obtains the optimal values ​​of the chilled water supply temperature, the air supply temperature of the air handling unit, and the cooling water supply temperature;

[0061] Step 9: After obtaining the optimal parameters, the fuzzy algorithm is used to output control commands to the execution unit of the ventilation and air-conditioning system to adjust the temperature and humidity in the station.

[0062] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A subway station ventilation and air conditioning energy-saving system based on load forecasting, characterized in that: include: The power monitoring unit is used to collect the real-time power parameters of the station's electrical equipment and upload them to the central control unit; Temperature and humidity monitoring unit, used to collect real-time temperature and humidity parameters inside and outside the station and the ventilation and air-conditioning system, and upload them to the central control unit; Passenger flow monitoring unit, used to collect passenger flow parameters entering and leaving the station and upload them to the central control unit; Wind speed monitoring unit, used to collect wind speeds of mechanical fresh air, supply air and return exhaust air, and upload them to the central control unit; Flow monitoring unit, used to collect the flow of chilled water and cooling water and upload it to the central control unit; The central control unit calculates the load forecast value of the station through intelligent algorithms based on real-time power parameters, real-time temperature and humidity parameters, passenger flow parameters, mechanical fresh air volume, and train timetable. The optimization algorithm with loss as the optimization target obtains the optimal values ​​of the chilled water supply temperature, the air supply temperature of the air handling unit, and the cooling water supply temperature, and then uses the fuzzy algorithm to output control commands to the chiller unit, the air handling unit unit, the cooling tower unit, the water pump unit, and the fan unit; The chiller unit is used to execute the control command of the central control unit and adjust the chilled water supply temperature to the optimal value; The air handling unit is used to execute the control command of the central control unit and adjust the air supply temperature of the air handling unit to the optimal value; The cooling tower unit is used to execute the control command of the central control unit and adjust the cooling water supply temperature to the optimal value; The water pump unit is used to execute the control command of the central control unit and adjust the flow of cooling water and chilled water; The fan unit is used to execute the control commands of the central control unit and adjust the air volume of fresh air, supply air, return air and exhaust air.

2. A subway station ventilation and air conditioning energy-saving system based on load forecasting according to claim 1, characterized in that: The electric energy monitoring unit comprises a current transformer and a multifunctional electricity meter.

3. The subway station ventilation and air conditioning energy-saving system based on load forecasting according to claim 1 is characterized in that: The temperature and humidity monitoring unit includes an off-site temperature and humidity sensor arranged at the inlet of the fresh air duct, a return air temperature and humidity sensor arranged in the return air duct, a supply air temperature and humidity sensor arranged after the air handling unit, a chilled water temperature sensor arranged at the inlet and outlet of the chiller, a cooling water temperature sensor arranged at the inlet and outlet of the cooling tower, and a wet-bulb temperature sensor arranged at the inlet of the cooling tower fan.

4. The subway station ventilation and air conditioning energy-saving system based on load forecasting according to claim 1 is characterized in that: The intelligent algorithms include cascade algorithm, BP algorithm and SVR algorithm.

5. The subway station ventilation and air conditioning energy-saving system based on load forecasting according to claim 1 is characterized in that: It also includes a meteorological correction unit, which corrects the meteorological data provided by the Meteorological Bureau according to the monitored temperature and humidity outside the station, so as to use the meteorological data provided by the Meteorological Bureau to predict the temperature and humidity outside the station in the next hour or the next day, and upload it to the central control unit.

6. A subway station ventilation and air conditioning energy saving method based on load forecasting, characterized in that: The following steps are involved: Step 1: Collect the real-time power parameters of the station's electrical equipment and upload them to the central control unit; Step 2: Collect the temperature and humidity outside the station, the temperature and humidity inside the station, the air supply temperature and humidity of the air handling unit, the supply and return water temperature of the chilled water, the supply and return water temperature of the cooling water, and the wet bulb temperature of the cooling tower inlet air, and upload them to the central control unit; Step 3: Collect the wind speed of mechanical fresh air, air handling unit supply air, and air conditioning system return and exhaust air, and upload them to the central control unit; Step 4: Collect the flow of chilled water and cooling water and upload them to the central control unit; Step 5: Collect real-time passenger flow and forecast hourly passenger flow in combination with train timetable and historical passenger flow data; Step 6: Use the meteorological correction unit to correct the meteorological data provided by the meteorological station and predict the temperature and humidity outside the station in the next time period; Step 7, the central control unit uses the real-time power parameters obtained through monitoring, the predicted temperature and humidity outside the station, the set temperature and humidity inside the station, the predicted hourly passenger flow, the mechanical fresh air volume, and the train timetable to calculate the station load prediction value through an intelligent algorithm; Step 8: After obtaining the station load forecast value, combine the real-time temperature and humidity parameters, wind speed and flow rate to avoid The optimization algorithm with loss as the optimization target obtains the optimal values ​​of the chilled water supply temperature, the air supply temperature of the air handling unit, and the cooling water supply temperature; Step 9: After obtaining the optimal parameters, the fuzzy algorithm is used to output control commands to the ventilation and air-conditioning system to adjust the temperature and humidity in the station.

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