Intelligent temperature control new energy storage air conditioner heat exchange system

Abstract

The invention relates to the technical field of energy storage air conditioners, and discloses an intelligent temperature control new energy energy storage air conditioner heat exchange system which comprises a photovoltaic power generation module and an energy storage system which are connected with the heat exchange system, and the heat exchange system is composed of a heat exchange module, an intelligent energy management module, a demand prediction module and an analysis module. The intelligent energy management module monitors the operation states of the whole system in real time, including the photovoltaic power generation condition, the state of the energy storage system and the energy output of the heat exchange module, and ensures that parameters of the system are dynamically adjusted in the operation process so as to match energy requirements and power generation conditions at different times, so that coordination among the systems is realized, and the energy efficiency is improved. And the demand prediction module analyzes historical power utilization data, weather forecast and seasonal changes, and can predict future energy demands, so that the system can make preparations in advance, and the beneficial effect of monitoring the coordination between the energy storage air conditioner heat exchange system and the energy storage system in real time is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage air conditioners, and in particular to an intelligent temperature-controlled new energy storage air conditioner heat exchange system. Background Art

[0002] Compared with conventional air conditioning systems, photovoltaic energy storage air conditioning systems can effectively reduce users' electricity bills on the basis of energy conservation and emission reduction. It mainly reduces electricity bills from four aspects: the improvement of the energy efficiency ratio of photovoltaic energy storage air conditioning systems, the role of energy storage batteries in shifting peaks and filling valleys, the benefits obtained from photovoltaic grid access and photovoltaic direct supply, and the reduction of electricity bills.

[0003] The prior art publication number is CN 115936516 A, which is an operating benefit evaluation method and system for a photovoltaic energy storage air conditioning system. The method includes: calculating the annual electricity bill of the building when conventional air conditioning is used and when photovoltaic air conditioning is used, the electricity bill saved by improving the energy efficiency of photovoltaic air conditioning throughout the year, the electricity bill saved by peak load shifting and valley filling by energy storage batteries, and the electricity bill saved by photovoltaic power generation according to the tiered electricity price of the city power grid, the hourly power consumption of other electrical equipment in the building, the hourly power consumption of conventional air conditioning throughout the year, and the hourly power consumption of photovoltaic air conditioning throughout the year; and evaluating the economic benefits of the photovoltaic energy storage air conditioning system according to the electricity bill savings. By adopting the technical solution of the present invention, the operating benefits of the photovoltaic energy storage air conditioning system can be evaluated, and the rationality of the selection of the photovoltaic energy storage air conditioning system can be improved.

[0004] Although the above-mentioned technical solution evaluates the economic benefits of the photovoltaic energy storage air-conditioning system through electricity bill savings and improves the rationality of the selection of the energy storage air-conditioning system, the coordination between the energy storage system and the heat exchange system is crucial. If the energy storage system cannot release energy in time during peak hours, the heat exchanger will not be able to provide sufficient energy when cooling or heating is needed. Summary of the invention

[0005] Technical issues solved:

[0006] In view of the shortcomings of the prior art, the present invention provides an intelligent temperature-controlled new energy energy storage air conditioning heat exchange system, which has the advantages of real-time monitoring of the coordination between the energy storage air conditioning heat exchange system and the energy storage system. The heat exchange system can predict and analyze peak hours and calculate the corresponding required energy to feed back to the energy storage system, thereby solving the problems of the above-mentioned technologies.

[0007] Technical solution:

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent temperature-controlled new energy storage air conditioning heat exchange system, comprising a photovoltaic power generation module and an energy storage system connected to the heat exchange system, wherein the heat exchange system is composed of a heat exchange module, an intelligent energy management module, a demand prediction module and an analysis module;

[0009] The heat exchange module is used to provide cooling and heating functions and adjust energy output according to demand;

[0010] The intelligent energy management module is used to monitor the operating status of the entire system in real time, including photovoltaic power generation, the status of the energy storage system, and the energy output of the heat exchange module;

[0011] The demand forecasting module is used to analyze historical data and use a machine learning-based forecasting model to analyze historical electricity consumption data, weather forecasts, and seasonal change factors to predict future energy demand;

[0012] The analysis module is used to optimize scheduling according to the energy demand analyzed by the demand prediction module.

[0013] Preferably, the energy output of the heat exchange module is calculated as:

[0014]

[0015] Where: Q is the energy output kW of the heat exchange module;

[0016] Q cool is the maximum energy output kW in heating mode;

[0017] Q heat is the maximum energy output kW in heating mode;

[0018] T set The target indoor temperature ℃ set by the user;

[0019] T current is the current actual indoor temperature in °C;

[0020] T min The lowest temperature that the system can cool to;

[0021] T max The maximum temperature that the system can heat up to ℃.

[0022] Preferably, the temperature difference of the heat exchange module is calculated as: ΔT = T set -T current ; where ΔT refers to the difference between two temperature values.

[0023] Preferably, in the heat exchange module: T set =24℃; T current =28℃; Q cool =5kW; T min =18℃; T max =30℃;

[0024] The energy output of the heat exchange module in cooling mode is calculated as:

[0025]

[0026] Preferably, the photovoltaic power generation power P in the intelligent source management module PV The calculation formula is:

[0027] P PV =A PV I solar ·η PV

[0028] Where: P PV is the effective area of ​​the photovoltaic panel m 2

[0029] I solar is the light intensity W / m 2 ;

[0030] η PV is the conversion efficiency of the photovoltaic panel.

[0031] Preferably, the calculation of the state of charge SOC of the energy storage system in the intelligent source management module is:

[0032]

[0033] Where: E current is the current amount of energy stored; E max is the maximum energy storage capacity of the battery;

[0034] The battery charging power P charge and discharge power P battery The calculation is:

[0035]

[0036] Where: E battery E is the discharge capacity; charge is the charging capacity; Δt is the time required for discharging or charging.

[0037] Preferably, the energy output of the heat exchange module in the intelligent energy management module is calculated as:

[0038] Energy output Q of heat exchange module output Calculated based on the current indoor and outdoor ambient temperatures and the set temperature:

[0039] In cooling mode:

[0040]

[0041] Where: Q coll is the maximum energy output in cooling mode; T set The target indoor temperature set by the user; Tcurrent is the current actual indoor temperature; T min The lowest temperature the system can cool to;

[0042] In heating mode:

[0043]

[0044] Where: Q heat is the maximum energy output in heating mode; T max This is the maximum temperature that the system can heat to.

[0045] Preferably, the prediction model in the demand prediction module is a linear regression model:

[0046] Y=β0+β1X1+β2X2+…+β n X n +∈

[0047] Where: Y is the predicted future energy demand kWh; X is the predicted future energy demand kWh; i are characteristic variables that affect energy demand: historical electricity consumption data, weather, season; β i is the regression coefficient, which indicates the influence of each characteristic variable on energy demand; ∈ is the error term, which indicates the random error of the prediction.

[0048] Preferably, the model evaluation in the demand forecasting module is calculated by mean square error:

[0049]

[0050] Where: MSE is the mean square error; Y i is the actual energy demand; is the predicted energy demand; N is the number of samples.

[0051] Preferably, the optimization target calculation in the analysis module is:

[0052] MinimizeC total =C operation +C maintenance +C renewable

[0053] Among them: Minimize means finding a scheduling solution that makes the sum of these costs as small as possible; C operation is the system operating cost; C maintenance is the system maintenance cost}; C renewable is the cost of renewable energy; C total Represents the target value to be optimized.

[0054] Compared with the prior art, the present invention provides an intelligent temperature-controlled new energy storage air conditioning heat exchange system, which has the following beneficial effects:

[0055] 1. The present invention monitors the operating status of the entire system in real time through the intelligent energy management module, including the photovoltaic power generation situation, the state of the energy storage system and the energy output of the heat exchange module, to ensure that the system dynamically adjusts parameters during operation to match the energy demand and power generation situation at different times, thereby achieving coordination between systems. The demand prediction module analyzes historical electricity consumption data, weather forecasts and seasonal changes. The demand prediction module can predict future energy demand, so that the system can prepare in advance and reasonably arrange energy output and energy storage, thereby reducing energy consumption and improving efficiency, ensuring that the energy storage system can meet the needs of the heat exchange system. The energy output calculation formula of the heat exchange module is closely related to the current indoor temperature, the set temperature and the maximum and minimum temperatures of the system, so that the heat exchange module dynamically adjusts the output according to the real-time environment and demand. The energy output of the heat exchange module through the calculated Q value can directly affect the charging and discharging strategy of the energy storage system. By calculating the charging state SOC to monitor the ratio of the current power of the energy storage system to the maximum capacity, the system can evaluate the state of the energy storage system in real time, ensuring that the energy storage system has enough power to discharge when the energy demand peaks, and vice versa, achieving the beneficial effect of real-time monitoring of the coordination between the energy storage air conditioning heat exchange system and the energy storage system.

[0056] 2. The present invention uses a prediction model based on machine learning to analyze historical electricity consumption data, weather forecasts and seasonal change factors, predict future energy demand, accurately predict peak hours, and make preparations in advance. The analysis module optimizes the energy scheduling plan based on the prediction data provided by the demand prediction module. Through optimized scheduling, the system can predict the peak energy demand in advance and reasonably plan energy supply and storage strategies. The energy output calculation formula of the heat exchange module takes into account the actual indoor temperature, set temperature, and the cooling and heating limit temperature of the system. This ensures that during peak hours, the heat exchange module can provide enough energy to meet system needs, achieving the beneficial effect that the heat exchange system can predict and analyze peak hours and calculate the corresponding required energy to feed back to the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 , intelligent temperature control new energy storage air conditioning heat exchange system, including a photovoltaic power generation module and an energy storage system connected to the heat exchange system, the heat exchange system consists of a heat exchange module, an intelligent energy management module, a demand prediction module and an analysis module;

[0060] The heat exchange module is used to provide cooling and heating functions, adjusting energy output according to demand;

[0061] The intelligent energy management module is used to monitor the operating status of the entire system in real time, including photovoltaic power generation, the status of the energy storage system, and the energy output of the heat exchange module;

[0062] The demand forecasting module is used to analyze historical data and use a machine learning-based forecasting model to analyze historical electricity consumption data, weather forecasts, and seasonal change factors to predict future energy demand.

[0063] The analysis module is used to optimize the scheduling according to the energy demand analyzed by the demand prediction module.

[0064] By connecting photovoltaic power generation modules, the system can use renewable energy to supply part or all of the energy needs, reducing dependence on traditional power grids and lowering carbon footprint.

[0065] Energy storage system: The energy storage system enhances the system's energy management capabilities, providing stable energy output during unstable power supply or peak hours, and optimizing energy use.

[0066] Intelligent energy management module: This module monitors the operating status of the entire system in real time, can dynamically adjust energy distribution and energy storage strategies, improve the adaptability and flexibility of the system, and ensure that the system can operate efficiently in various environments and working conditions.

[0067] Demand forecasting module: By analyzing historical and real-time data, the system can predict future energy demand, schedule and reserve energy in advance, and ensure that the system has sufficient energy supply during high-demand periods.

[0068] Heat exchange module: Provide customized cooling and heating services according to user needs, adjust energy output in real time, ensure that the indoor environment always remains in a comfortable state, and improve user comfort and satisfaction.

[0069] Analysis module: By optimizing the scheduling strategy, the system can more accurately meet user needs, reduce energy waste, and improve user experience.

[0070] Utilization of renewable energy: The use of photovoltaic power generation modules significantly improves the system's utilization of renewable energy, reduces dependence on fossil fuels, and helps mitigate climate change.

[0071] Energy saving and consumption reduction: Through intelligent energy management and optimized scheduling, the system can significantly reduce energy consumption, reduce operating costs, and at the same time reduce environmental pollution to achieve green environmental protection goals.

[0072] Specifically, the energy output of the heat exchange module is calculated as:

[0073]

[0074] Where: Q is the energy output kW of the heat exchange module;

[0075] Q cool is the maximum energy output kW in heating mode;

[0076] Q heat is the maximum energy output kW in heating mode;

[0077] T set The target indoor temperature ℃ set by the user;

[0078] T current is the current actual indoor temperature in °C;

[0079] T min The lowest temperature that the system can cool to;

[0080] T max The maximum temperature that the system can heat up to ℃.

[0081] Specifically, the temperature difference of the heat exchange module is calculated as: ΔT = T set -T current ; where ΔT refers to the difference between two temperature values.

[0082] The prediction model based on machine learning is used to analyze historical electricity consumption data, weather forecasts and seasonal change factors, predict future energy demand, accurately predict peak hours, and prepare in advance. The analysis module optimizes the energy scheduling plan based on the prediction data provided by the demand prediction module. Through optimized scheduling, the system can predict the peak of energy demand in advance and reasonably plan energy supply and storage strategies. The energy output calculation formula of the heat exchange module takes into account the actual indoor temperature, set temperature, and the cooling and heating limit temperature of the system. This ensures that during peak hours, the heat exchange module can provide enough energy to meet system needs.

[0083] Specifically, in the heat exchange module: T set =24℃; T current =28℃; Q coll =5kW; T min =18℃; T max =30℃;

[0084] The energy output of the heat exchange module in cooling mode is calculated as:

[0085]

[0086] Specifically, the photovoltaic power generation power P in the intelligent source management module PV The calculation formula is:

[0087] P PV =A PV I solar ·η PV

[0088] Where: P PV is the effective area of ​​the photovoltaic panel m 2

[0089] I solar is the light intensity W / m 2 ;

[0090] η PV is the conversion efficiency of the photovoltaic panel.

[0091] Specifically, the calculation of the charging state SOC of the energy storage system in the intelligent source management module is:

[0092]

[0093] Where: E current is the current amount of energy stored; E max is the maximum energy storage capacity of the battery;

[0094] Battery charging power P charge and discharge power P battery The calculation is:

[0095]

[0096] Where: E battery E is the discharge capacity; charge is the charging capacity; Δt is the time required for discharging or charging.

[0097] Specifically, the energy output of the heat exchange module in the intelligent energy management module is calculated as:

[0098] Energy output Q of heat exchange module output Calculated based on the current indoor and outdoor ambient temperatures and the set temperature:

[0099] In cooling mode:

[0100]

[0101] Where: Q coolis the maximum energy output in cooling mode; T set The target indoor temperature set by the user; T current is the current actual indoor temperature; T min The lowest temperature the system can cool to;

[0102] In heating mode:

[0103]

[0104] Where: Q heat is the maximum energy output in heating mode; T max This is the maximum temperature that the system can heat to.

[0105] Specifically, the prediction model in the demand prediction module is a linear regression model:

[0106] Y=β0+β1X1+β2X2+…+β n X n +∈

[0107] Where: Y is the predicted future energy demand kWh; X is the predicted future energy demand kWh; i are characteristic variables that affect energy demand: historical electricity consumption data, weather, season; β i is the regression coefficient, which indicates the influence of each characteristic variable on energy demand; ∈ is the error term, which indicates the random error of the prediction.

[0108] Specifically, the model evaluation in the demand forecasting module is calculated by the mean square error:

[0109]

[0110] Where: MSE is the mean square error; Y i is the actual energy demand; is the predicted energy demand; N is the number of samples.

[0111] Specifically, the optimization target calculation in the analysis module is:

[0112] MinimizeC total =C operation +C maintenance +C renewable

[0113] Among them: Minimize means finding a scheduling solution that makes the sum of these costs as small as possible; C operation is the system operating cost; C maintenance is the system maintenance cost}; C renewable is the cost of renewable energy; C total Represents the target value to be optimized.

[0114] The intelligent energy management module monitors the operating status of the entire system in real time, including photovoltaic power generation, the status of the energy storage system and the energy output of the heat exchange module, ensuring that the system dynamically adjusts parameters during operation to match energy demand and power generation at different times, thereby achieving coordination between systems. The demand forecasting module analyzes historical electricity consumption data, weather forecasts and seasonal changes. The demand forecasting module can predict future energy demand, enabling the system to prepare in advance and reasonably arrange energy output and energy storage, thereby reducing energy consumption and improving efficiency, ensuring that the energy storage system can meet the needs of the heat exchange system. The energy output calculation formula of the heat exchange module is closely related to the current indoor temperature, set temperature and the maximum and minimum temperatures of the system, so that the heat exchange module dynamically adjusts the output according to the real-time environment and demand. The energy output of the heat exchange module can directly affect the charging and discharging strategy of the energy storage system through the calculated Q value. The ratio of the current power of the energy storage system to the maximum capacity is monitored by calculating the charging state SOC, so that the system can evaluate the status of the energy storage system in real time, ensuring that the energy storage system has enough power to discharge when the energy demand peaks, and vice versa.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. Intelligent temperature-controlled new energy storage air conditioning heat exchange system, including a photovoltaic power generation module and an energy storage system connected to the heat exchange system, characterized in that: The heat exchange system consists of a heat exchange module, an intelligent energy management module, a demand prediction module and an analysis module; The heat exchange module is used to provide cooling and heating functions and adjust energy output according to demand; The intelligent energy management module is used to monitor the operating status of the entire system in real time, including photovoltaic power generation, the status of the energy storage system, and the energy output of the heat exchange module; The demand forecasting module is used to analyze historical data and use a machine learning-based forecasting model to analyze historical electricity consumption data, weather forecasts, and seasonal change factors to predict future energy demand; The analysis module is used to optimize scheduling according to the energy demand analyzed by the demand prediction module.

2. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 1 is characterized in that: The energy output of the heat exchange module is calculated as: Where: Q is the energy output kW of the heat exchange module; Q cool is the maximum energy output kW in heating mode; Q heat is the maximum energy output kW in heating mode; T set The target indoor temperature ℃ set by the user; T current is the current actual indoor temperature in °C; T min The lowest temperature that the system can cool to; T max The maximum temperature that the system can heat up to ℃.

3. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 2 is characterized in that: The temperature difference of the heat exchange module is calculated as: ΔT = T set -T current ; where ΔT refers to the difference between two temperature values.

4. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 3 is characterized in that: In the heat exchange module: T set =24℃; T current =28℃; Q cool =5kW; T min =18℃; T max =30℃; The energy output of the heat exchange module in cooling mode is calculated as:

5. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 4 is characterized in that: The photovoltaic power generation power P in the intelligent source management module PV The calculation formula is: P PV =A PV ·I solar ·or PV Where: P PV is the effective area of ​​the photovoltaic panel m 2 I solar is the light intensity W / m 2 ; η PV is the conversion efficiency of the photovoltaic panel.

6. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 5 is characterized in that: The calculation of the state of charge SOC of the energy storage system in the intelligent source management module is: Where: E current is the current amount of energy stored; E max is the maximum energy storage capacity of the battery; The charging power P of the battery charge and discharge power P batter The calculation of y is: Where: E battery E is the discharge capacity; charge is the charging capacity; Δt is the time required for discharging or charging.

7. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 6 is characterized in that: The energy output of the heat exchange module in the intelligent energy management module is calculated as: Energy output Q of heat exchange module output Calculated based on the current indoor and outdoor ambient temperatures and the set temperature: In cooling mode: Where: Q cool is the maximum energy output in cooling mode; T set The target indoor temperature set by the user; T current is the current actual indoor temperature; T min The lowest temperature the system can cool to; In heating mode: Where: Q heat is the maximum energy output in heating mode; T max This is the maximum temperature that the system can heat to.

8. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 7 is characterized in that: The prediction model in the demand prediction module is a linear regression model: Y=β0+β1X1+β2X2+…+β n X n +∈ Where: Y is the predicted future energy demand kWh; X is the predicted future energy demand kWh; i are characteristic variables that affect energy demand: historical electricity consumption data, weather, season; β i is the regression coefficient, which indicates the influence of each characteristic variable on energy demand; ∈ is the error term, which indicates the random error of the prediction.

9. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 8 is characterized in that: The model evaluation in the demand forecasting module is calculated by the mean square error: Where: MSE is the mean square error; Y i is the actual energy demand; is the predicted energy demand; N is the number of samples.

10. The intelligent temperature-controlled new energy storage air conditioning heat exchange system according to claim 9 is characterized in that: The optimization target calculation in the analysis module is: MinimizeC total =C operation +C maintenance +C renewable Among them: Minimize means finding a scheduling solution that makes the sum of these costs as small as possible; C operation is the system operating cost; C maintenance is the system maintenance cost}; C renewable is the cost of renewable energy; C total Represents the target value to be optimized.

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