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With the continuous development of the Internet of Things (IoT) and new energy utilization technology, the energy management and monitoring mode has undergone fundamental changes. Design and development of remote realtime monitoring and management system for multienergy heat collection has certain theoretical and practical application value. The existing remote temperature control model fully considers neither the impact of multienergy collaborative heat collection on remote temperature control strategy, nor the interactions among multiple objective factors, such as climate and location. Therefore, this paper studied the remote temperature control strategy of multienergy heat collection based on the IoT. First, the paper gave the application architecture of multienergy heat collection system functions, and developed a multienergy heat collection system model without simulation performance loss using stochastic modeling ideas. Second, this paper made full use of the error information of the process, thus correcting the predicted value of the remote temperature control output. Finally, the experimental results verified the effectiveness of the model and the control strategy.
Internet of Things (IoT), multienergy, heat collection, remote temperature control
In the tense global energy situation today, countries around the world are seeking new energy alternative strategies, and clean energy has become the focus of attention due to its advantages of continuous supply and security. However, clean energy has low energy density and is easily affected by objective factors, such as climate and location, which limits its effective use [18]. However, with the continuous development of the IoT and new energy utilization technology, the comprehensive utilization of clean energy has been brought into full play, and the energy management and monitoring mode has also undergone fundamental changes [915]. The market has unanimously recognized and promoted the multienergy collaborative heat collection technology, such as solar energy combined with wind power generation, water source heat pump, groundsource heat pump, gasfired heat energy and so on [1621]. Starting from the actual application requirements, there is certain theoretical and practical application value to design and develop a remote realtime monitoring and management system for multienergy heat collection, as well as to manage and control the temperature data information of the system in a remote, efficient, realtime and accurate way.
With the increase of distributed energy resources, the Multiple Energy System (MES) has attracted more and more attention in the academic world. The uncertainty of wind and solar power generation should be taken into consideration because the MES usually has a high penetration rate of renewable energy. Considering the uncertainty of wind and solar power generation, Hu and He [22] proposed a dayahead economic scheduling model for parklevel multienergy system with heat and gas energy storage and high permeability renewable energy. The mathematical model was established by MATLAB and solved by Gurobi. The study observed the change trend and operation mode of the MES costs under different renewable energy uncertainties, and monitored the operation mode of a park when there was energy storage equipment. The conclusion was that heat and gas storage equipment helped solve the impact of renewable energy uncertainties in the MES. van der Roest et al. [23] developed a method to improve the integration and control of hightemperature aquifer thermal energy storage (HTATES) system in a decentralized multienergy system. The study expanded the multienergy system model and combined it with the numerical hydrothermal model in order to dynamically simulate the functions of multiple HTATES system design. The authors did the research on 2,000 households. The results showed that the HTATES integration with powertoheat (PtH) allowed to supply 100% annual heat demand. With the best economic benefits or the lowest pollution emission costs of the whole system operation as the optimization objective, Fu et al. [24] analyzed the network balance equation and branch characteristic equation of the heating network model, and used the effect of transmission loss as a constraint, based on the existing electricity and heat coordinated operation and scheduling model. Finally, the study analyzed through examples the influence of network transmission loss on optimization results with different optimization objectives. He et al. [25] established a multiobjective optimization model, including exergy loss and operation costs, and a heat network model by considering time delay, loss and various heat loads. In addition, the study proposed a multiEH and multiobjective coordinated operation framework considering the heat network model, and gave a method for solving Pareto front and a reference method for selecting the final result based on Nash bargaining. For multienergy flow system, the introduction of nodal energy price not only improved the economy of the whole system, but also provided certain ideas for managing energy network congestion. Yang et al. [26] first gave the calculation method of nodal price by referring to the price, and then established the optimal scheduling model of multienergy flow system considering the influence factors, such as transmission loss of heating network and so on. Finally, the study calculated the nodal energy price based on examples, and analyzed the impact of network loss constraint on the nodal energy price.
This paper summarized two defects of the current remote temperature control model for multienergy heat collection. First, the existing model has a huge and complex structure and requires a large amount of computation. In addition, design of the controller is cumbersome. Second, the existing model fully considers neither the impact of multienergy collaborative heat collection on the remote temperature control strategy, nor the interactions among multiple objective factors, such as climate, location, etc., which cannot be effectively applied to the remote temperature control. Therefore, this paper studied the remote temperature control strategy of multienergy heat collection based on the IoT. Chapter 2 gave the application architecture of the system functions, and developed a related model without losing simulation performance using stochastic modeling ideas. Chapter 3 made full use of the error information of the process, thus correcting the predicted value of the remote temperature control output. Finally, the experimental results verified that the constructed model and the control strategy were effective.
Figure 1. Application architecture of multienergy heat collection system functions
Figure 2. Monitoring mode structure of multienergy heat collection system
Table 1. Information on the adopted sensors
Model 
Purpose 
Range of measurement signal 
Type of output signal 
LWGB turbine transmitter 
Measurement of flow 

4~20 mA 
HTDSL206 pressure sensor 
Measurement of water pressure 
Choose from 100 KPa~0.6 Mpa ~60 Mpa~120 Mpa 
420 mA (twowire system), 0~5 VDC, 0~10 VDC, 0.54.5 VDC (threewire system) 
XFHTF200FPB series input static pressure level gauges 
Measurement of water level 
00.5M~1M~3M ~5M~10M~20M ~50M~100M~200M (Water level height/depth, with 0.5 m as the minimum range) 
4~20 mA, 0~10 mA (twowire system), 0~5 VDC, 0~10 VDC, 0.5~4.5 VDC (threewire system) 
KZWJPTA integrated temperature sensor 
Measurement of water temperature 
200450℃, accuracy ± 0.2% 
Twowire system 4~20 mA 
Figure 1 shows the application architecture of six multienergy heat collection system functions, namely, remote realtime monitoring, sensor monitoring and management, heat collection supply document management, operation and maintenance management module, equipment management, and data statistics and analysis. Three modes, Browser/Server (B/S), Client/Server (C/S) and APP, were used for the hybrid construction of the remote monitoring architecture of the system. Figure 2 shows the detailed implementation process of the monitoring architecture mode.
The mainly detected data of multienergy heat collection system included four aspects, namely, water temperature, water level, water pressure and flow. Table 1 shows the information on the adopted sensors, that is, the model, purpose and output signal characteristics of the sensors.
For the IoTbased remote temperature control of multienergy heat collection, an accurate model is usually required to derive an effective controller. However, due to the inaccurate simulation performance, the traditional model is not suitable for the remote temperature control design. Therefore, this paper developed a multienergy heat collection system model without losing simulation performance using stochastic modeling ideas.
The heat generated by clean energy was stochastic rather than deterministic, leading to indeterministic data measured by sensors. Stochastic model accurately reflected the temperature changes of the system. At the same time, the stochastic disturbance of objective factors, such as climate and location, might help stabilize the system.
This paper focused on the temperature stochastic modeling and parameter estimation of multienergy heat collection system. Considering the disturbance of heat, produced by clean energy, by objective factors, such as climate and location, based on the assumption of independent and uniformly distributed disturbance, this paper constructed a stochastic differential equation model of the system, and estimated the drift and diffusion terms using the maximum likelihood estimation method.
Let σ_{water }be the density of tap water, D_{e,water }be the specific heat capacity of tap water, U be the volume of tap water participating in heat exchange in the system, O_{im }be the water inlet temperature of the system, W_{cov}_{}_{water }be the heat flux between solar energy heat collection input module and internal tap water in the system, W_{flr}_{}_{water }be the heat flux between external air and internal tap water, W_{ass}_{}_{water }be the heat flux between other clean energy auxiliary heat energy modules and internal tap water, and W_{vent }be the heat flux loss of tap water caused by objective factors. The following formula gave the duration temperature model expression of the system:
$\begin{aligned} & \frac{\sigma_{\text {water }} D_{e, \text { water }} U d O_{\text {in }}(o)}{d o}= W_{\text {cov_water }}+W_{\text{flr_water}}+W_{\text {ass_water }}+W_{\text {vent }}\end{aligned}$ (1)
Let X_{d }be the solar panel coverage area of the system, X_{h }be the heat exchange between solar panel and internal tap water, and O_{out }be the outlet water temperature of the system. According to the thermodynamic principle, the calculation formula of the heat flux between the solar energy heat collection input module and the internal tap water in the system was:
${{W}_{\operatorname{cov}\_water}}=1.7\frac{{{X}_{d}}}{{{X}_{h}}}{{\left {{O}_{out}}{{O}_{in}} \right}^{0.31}}\left( {{O}_{out}}{{O}_{in}} \right)$ (2)
Let O_{flr }be the external air temperature of the system, and l_{flr}_{}_{water }be the temperature transfer coefficient from external air to tap water in the system. Similarly, the heat flux calculation formula between external air and internal tap water was constructed as follows:
${{W}_{flr\_water}}={{l}_{flr\_water}}{{X}_{h}}\left( {{O}_{flr}}{{O}_{in}} \right)$ (3)
l_{flr}_{}_{water }was obtained from the following formula:
${{l}_{flr\_air}}=\left\{ \begin{align} & 1.7{{\left( {{O}_{in}}{{O}_{flr}} \right)}^{0.33}},{{O}_{flr}}>{{O}_{in}} \\ & 1.3{{\left( {{O}_{in}}{{O}_{flr}} \right)}^{0.25}},{{O}_{flr}}<{{O}_{in}} \\ \end{align} \right.$ (4)
Let δ_{water }be the heat energy absorption coefficient of tap water, and SE_{glob }be the heat energy supply of other clean energy resources. Then the heat flux between the auxiliary heat energy module of other clean energy resources and the internal tap water was calculated as follows:
${{W}_{ass\_water}}={{\delta }_{water}}\cdot {{X}_{d}}\cdot S{{E}_{glob}}$ (5)
Let Ψ_{vent }be the occurrence probability of objective factors, then there was the following formula for calculating the heat flux loss of tap water caused by those factors:
${{W}_{vent}}={{\sigma }_{water}}{{D}_{e,water}}{{\Psi }_{vent}}\left( {{O}_{out}}{{O}_{in}} \right)$ (6)
It was difficult to calculate Ψ_{vent} accurately considering the influence of objective factors, such as season, climate and location. Let α_{TC }and ω_{TC }be the undetermined constants, and the disturbance of objective factors was regarded as the derivative of Brownian motion Y(o), that is, Y(o)=dY(o)/do, then there was:
${{W}_{vent}}={{\sigma }_{water}}{{D}_{e,water}}\alpha _{TC}^{{}}\left( {{O}_{out}}{{O}_{in}} \right)+{{\omega }_{TC}}Y\left( o \right)$ (7)
The above formula was transformed into the form of nonlinear stochastic differential equation:
$d{{O}_{in}}\left( o \right)=g\left( {{O}_{out}},{{O}_{in}},{{O}_{flr}} \right)do+{{\omega }_{TC}}dY\left( o \right)$ (8)
where,
$\begin{aligned} & g\left(O_{\text {out }}, O_{\text {in }}, O_{f l r}\right)= \frac{1.7 X_d}{\sigma_{\text {air }} D_{e, \text { water }} U X_h}\leftO_{\text {out }}O_{\text {in }}\right^{0.31}\left(O_{\text {out }}O_{\text {in }}\right) +\frac{l_{f l_{\text {water }}} X_g}{\sigma_{\text {water }} D_{e, \text { water }} U}\left(O_{f t t}O_{\text {in }}\right) +\frac{\delta_{\text {water }} \cdot X_d \cdot S E_{\text {glob }}}{\sigma_{\text {air }} D_{e, \text { water }} U X_h}+\frac{\alpha_{T C}}{U}\left(O_{\text {out }}O_{\text {in }}\right)\end{aligned}$ (9)
At the same time, when the continuous time path of parameters in the system was observed at equidistant time points, let Δo be the discretization step and ρ_{i }be the independent parameter sequence M(0,1), then, there was the following based on formula 8:
$\begin{align} & O_{in}^{i}=O_{in}^{ik}+g\left( O_{out}^{ik},O_{in}^{ik},O_{flr}^{ik} \right)\Delta o +{{\omega }_{TC}}\sqrt{\Delta o{{\rho }_{i}}},i=1,2,... \\ \end{align}$ (10)
Let O_{0}, O_{1}, O_{2}...O_{m }be the parameter observation sequence based on the above formula, and G_{i}_{1}=ε(O_{j},j≤i1), then the conditional probability density function expression of G_{i}_{1},O_{i }was given by the following formula:
$\begin{aligned} & g\left(O_i / G_{i1}\right)=\frac{1}{\sqrt{2 \pi \Delta o} \omega_{T C}} \exp \left\{\frac{1}{2 \omega_{T C \Delta o}^2}\left[O_{\text {in }}^iO_{\text {out }}^{il}g\left(O_{\text {out }}^{il}, O_{i n}^{il}, O_{f r i}^{i1}\right) \Delta o\right]^2\right\}\end{aligned}$ (11)
For the given G_{0}, the following formula gave the joint conditional probability density function expression of (O_{1}, O_{2}...O_{m}):
$\begin{aligned} & \left(O_1, O_2, \ldots, O_m \mid G_0\right)=\left(\frac{1}{\sqrt{2 \pi \Delta o} \omega_{T C}}\right)^n \\ & \coprod_{i=1}^n \exp \left\{\frac{1}{2 \theta_{T C \Delta o}^2}\left[\begin{array}{l}O_{\text {in }}^iO_{\text {out }}^{il} \\ g\left(O_{\text {out }}^{ik}, O_{\text {in }}^{i1}, O_{f l r}^i\right) \Delta o\end{array}\right]^2\right\}\end{aligned}$ (12)
The loglikelihood function was given by the following formula:
$\begin{aligned} & K_m\left(\alpha_{T C}, \omega_{T C}\right)=\frac{m}{2} \log 2 \pi \Delta om \log \omega_{T C} \frac{1}{2 \omega_{T C}^2 \Delta o} \sum_{i=1}^m\left[\begin{array}{l}O_{\text {in }}^iO_{o u t}^{i1} \\ g\left(O_{\text {out }}^{i1}, O_{\text {in }}^{i1}, O_{f l r}^i\right) \Delta o\end{array}\right]^2\end{aligned}$ (13)
$\left\{\begin{array}{l}\frac{\partial K_m\left(\alpha_{T C}, \omega_{T C}\right)}{\partial \phi_{T C}}=0 \\ \frac{\partial K_m\left(\alpha_{T C}, \omega_{T C}\right)}{\partial \omega_{T C}}=0\end{array}\right.$ (14)
Based on the above analysis, the following was obtained:
$\left\{\begin{array}{l}\sum_{i=1}^m\left[O_{\text {in }}^iO_{\text {out }}^{i1}g\left(O_{\text {out }}^{i1}, O_{\text {in }}^{i1}, O_{f l r}^{i1}\right) \Delta o\right]=0 \\ \frac{m}{\omega_{T C}}+\frac{1}{3 \omega_{T C}^3 \Delta o} \sum_{i=1}^m\left[\begin{array}{l}O_{\text {in }}^iO_{\text {out }}^{i1} \\ g\left(O_{\text {out }}^{i1}, O_{i n}^{i1}, O_{f l r}^{i1}\right) \Delta o\end{array}\right]^2=0\end{array}\right.$ (15)
Further, the following was obtained:
$\left\{\begin{array}{l}\hat{\alpha}_{T C}=\frac{\sum_{i=1}^m\left[O_{i n}^iO_{i n}^{i1}Y_{i1}\right]}{\sum_{i=1}^m\left(O_{\text {out }}^iO_{i n}^{i1}\right) \Delta o} \\ \hat{\omega}_{T C}=\sqrt{\frac{\sum_{i=1}^m\left[O_{i n}^iO_{\text {out }}^{i1}g\left(O_{\text {in }}^i, O_{\text {out }}^{i1}g\left(O_{o u t}^{i1}, O_{\text {in }}^{i1}, O_{\text {flr }}^i\right) \Delta o\right)\right]^2}{3 m \Delta o}}\end{array}\right.$ (16)
where,
$\begin{aligned} & Y_{i1}=\frac{1.7 X_d}{\sigma_{\text {water }} D_{e, \text { water }} U X_h}\leftO_{o u t}^{i1}O_{i m}^{i1}\right^{0.31} \left(O_{\text {out }}^{i1}O_{i n}^{i1}\right)+\frac{l_{f l r_{a i r}} X_h}{\sigma_{\text {water }} D_{e, \text { water }} U}\left(O_{f l r}^{i1}O_{i n}^{i1}\right) +\frac{\delta_{\text {water }} \cdot X_d \cdot S E_{\text {glob }}}{\sigma_{\text {water }} D_{e, \text { water }} U X_h}\end{aligned}$ (17)
After controlling the multienergy heat collection system at time l, the predicted value of the system at the next time might deviate from the actual value, because the actual remote temperature control process was easily affected by objective factors (season, climate, location, etc.) and unknown uncertain factors (model error, timevarying, etc.). In order to correct this situation, it was necessary to fully consider the realtime information of multienergy heat collection system, and to conduct feedback correction on the remote temperature control system, thus avoiding the occurrence of false remote temperature control system solution values, and at the same time reducing the divergence probability of remote temperature control system. Therefore, this paper chose to make full use of the error information of the process, instead of correcting after all the temperature control increments worked, thus completing the correction of the output predicted value. The following formula gave the expression of the first control action, among the first implemented control increment Δv_{N} (l) for N time starting from the current time, at time o=lO:
$\begin{aligned} & \Delta v(l)=d^T \Delta v_N(l)= d^T\left(X^T W X+S\right)^{1} X^T W\left[\theta_e(l)\tilde{b}_{E 0}(l)\right] =c^T\left[\theta_e(l)\tilde{b}_{E 0}(l)\right]\end{aligned}$ (18)
$v\left( l \right)=v\left( l1 \right)+\Delta v\left( l \right)$ (19)
${{c}^{T}}={{D}^{T}}{{\left( {{X}^{T}}W+X \right)}^{1}}{{S}^{T}}X={{W}_{1}}\left( {{c}_{2}}\cdot {{c}_{E}} \right)$ (20)
Let Δv_{N}(l) be the optimal solution at the time of o=(l+1)O, ∆v(l+1) be the second component, b(l+1) be the actual output of the multienergy heat collection system object, and ḃ_{1}(l+kl) be the prediction output of the model, which is the first component of ḃ_{M}_{1}(l). The output prediction of the system in the future was mainly affected by the control increment ∆v(l), because Δv(l) was already applied to the system object. Therefore, b(l+1) should be detected and compared with ḃ_{1}(l+kl), instead of continuing to implement ∆v(l+1), thus forming the output error.
$p\left( l+1 \right)=b\left( l+1 \right){{\dot{b}}_{1}}\left( l+1l \right)$ (21)
The output error information reflected the influence of objective factors and unknown uncertain factors (model mismatch, timevarying, etc.) on the system output. Considering the existence of prediction error, the output value prediction of the remote temperature control system at all time also needed to be corrected. This paper predicted the future output error using the time series method. Let p(l+1) be the realtime error of the remote temperature control system, and ḃ_{CE}(l+1) be the output of the predicted system at time o=(l+i)O(i=1,...,M) after error correction at time o=(l+1)O, then ḃ_{CE}(l+1) was obtained by weighting the coefficient f_{i}(i1,2,... ,M) on p(l+1):
${{\dot{b}}_{CE}}\left( l+1 \right)={{\dot{b}}_{N1}}\left( l \right)+fp\left( l+1 \right)$ (22)
$\dot{b}_{C E}(l+1)=\left[\begin{array}{l}\dot{b}_{C E}(l+1 \mid l+1) \\ \vdots \\ \dot{b}_{C E}(l+M \mid l+1)\end{array}\right]$ (23)
where, the error correction vector f was:
$f=\left[\begin{array}{l}f_1 \\ \vdots \\ f_M\end{array}\right]$ (24)
According to the above derivation, after being corrected, except the first item, other items in ḃ_{CE}(l+1) are the predicted values of system output of time o=(l+2)O without the influence of Δv(l+1) at time o=(l+1)O. Those items were regarded as the first M1 components of ḃ_{M}_{0}(l+1) at time o=(l+1)O, that is:
$\begin{aligned} & \dot{b}_0(l+1+i \mid l+1)=\dot{b}_{C E}(l+1+i \mid l+1), i=1, \ldots, M1\end{aligned}$ (25)
The prediction of the output of i=(l+1+M)O at time o=(l+1)O was the last component of ḃ_{M}_{0}(l+1). Due to the influence of model truncation, the prediction was approximated by ḃ_{0}(l+1l+1). That is, ḃ_{M}_{0}(l+Ml+1) was expressed using the vector form in the following formula:
$\dot{b}_{M 0}(l+1)=R \cdot \dot{b}_{C E}(l+1)$ (26)
ḃ_{M}_{0}(l+1) was obtained at time o=(l+1)O, which pushed ahead the control process of remote temperature control system, thus optimizing the temperature control strategy at time l+1. The whole control continued to move forward, jointly affected by the model optimization of multienergy heat collection system and feedback correction. Figure 3 shows the proportional control diagram of temperature control time in different working conditions.
According to the above analysis, the remote temperature control strategy proposed in this paper was divided into three parts: prediction, control and correction. Based on Δv(l) of each sampling time, the control quantity v(l) was calculated through digital integration operation and applied to the system object, and ḃ_{M}_{1}(l) was also calculated through multiplication operation with step response vector. At the next sampling time, b(l+k) was first detected and the output error p(l+1) was calculated. As the control process progressed, the corrected ḃ_{CE}(l+1) shifted to generate a new initial prediction value ḃ_{M}_{0}(l+1). The new time was redefined as l time by the timedisplacement operator. The calculation of the control increment at the new time was completed based on the expected system output and the components of the initial predicted value ḃ_{M}_{0}(l). The remote temperature control process of multienergy heat collection was repeated online through this cycle. Figure 4 shows the flow chart of the main program.
Figure 3. Schematic diagram of proportional control of temperature control time in different working conditions
Figure 4. Flow chart of temperature control main program
Figure 5. Water tank outlet temperature curve in all working conditions
According to the remote temperature control mode of multienergy heat collection proposed in this paper, Figure 5 shows the outlet temperature change curve of water tank in all working conditions of the system. According to the experiment, with the influence of objective factors (season, climate and location, etc.) and unknown uncertain factors (model error, timevarying, etc.), the system control kept the outlet temperature of water tank constant in each working condition, and the temperature fluctuation was controlled within the preset range.
According to the disturbance degree of objective and uncertain factors and without considering the interactions of those factors, this paper selected the calculation orthogonal table of control increment at the new time to determine the experimental scheme. Table 2 shows the proportional, integral and derivative (PID) parameters of the thyristor controlling the system heat flow and the mean square error of inlet temperature of the heat collection tank in all schemes.
Table 2. Experimental results of remote temperature control strategy of multienergy heat collection
Factors Test No. 
Maximum voltage (V) 
P 
1 
D 
Inlet temperature variance 
1 
2 
3 
4 

1 
1(2.5) 
1(50) 
1(3) 
1(1) 
0.01097 
2 
1(2.5) 
2(55) 
2(3.5) 
2(1.5) 
0.243753 
3 
1(2.5) 
3(60) 
3(4) 
2(1.5) 
0.11257 
4 
2(2.6) 
1(50) 
1(3) 
3(2) 
0.025006 
5 
2(2.6) 
2(55) 
2(3.5) 
1(1) 
0.017863 
6 
2(2.6) 
3(60) 
3(4) 
1(1) 
0.064259 
7 
3(2.7) 
1(50) 
1(3) 
2(1.5) 
0.029923 
8 
3(2.7) 
2(55) 
2(3.5) 
2(1.5) 
0.03794 
9 
3(2.7) 
3(60) 
3(4) 
3(2) 
0.26751 
10 
4(2.8) 
1(50) 
1(3) 
1(1) 
0.03672 
11 
4(2.8) 
2(55) 
2(3.5) 
1(1) 
0.029754 
12 
4(2.8) 
3(60) 
3(4) 
2(1.5) 
0.039252 
13 
5(2.9) 
1(50) 
1(3) 
2(1.5) 
0.036411 
14 
5(2.9) 
2(55) 
2(3.5) 
3(2) 
0.034829 
15 
5(2.9) 
3(60) 
3(4) 
1(1) 
0.035366 
16 
6(3.0) 
1(50) 
1(3) 
2(1.5) 
0.037524 
17 
6(3.0) 
2(55) 
2(3.5) 
3(2) 
0.041916 
18 
6(3.0) 
3(60) 
3(4) 
1(1) 
0.036136 
1) Test node temperature
2) Temperature error
Figure 6. Model test results
Table 3. Variance analysis of multienergy heat collection remote temperature control experiment
Level 
Maximum voltage 
P 
I 
D 
1 
0.12236 
0.02976 
0.02954 
0.03517 
2 
0.03521 
0.06754 
0.06751 
0.0619 
3 
0.03157 
0.0568 
0.0536 
0.04571 
4 
0.0634 



5 
0.03576 



6 
0.03815 



Delta 
0.0906 
0.03712 
0.03851 
0.03526 
Rank 
1 
3 
2 
4 
Figure 6 shows the model test results, that is, the error values of measured and predicted temperature and theoretical temperature at different test nodes. The actual measured and predicted temperature of the test node was closely related to the solar radiation value, because the solar energy collection module in the multienergy heat collection system was the core heating module, and other clean energy modules were the auxiliary thermal energy modules. The figure shows that the temperature of the system in this experiment changes with time, which verified the effectiveness of the model in this paper in the remote temperature control results of the system.
The data of 18 groups of experimental results were further analyzed. Table 3 shows the variance analysis results of the remote temperature control experiment.
According to the table, when the thermal flow of the system was controlled by the thyristor, the analog input was the main factor of remote temperature control of multienergy heat collection, and the PID control parameters were the secondary factor of temperature control. Based on the variance analysis results, the optimal multienergy heat collection cooperation strategy was obtained. That is, the analog input maximum voltage of thyristor was 2.6 V, and the PID control parameters were 51, 4, and 2.
According to the analysis results, when the heat produced by clean energy was disturbed by objective factors, such as climate and location, the controlled multienergy heat collection system had rapid temperature response, short adjustment time, and relatively ideal control effect and robust performance.
This paper studied the IoTbased remote temperature control strategy of multienergy heat collection. First, this paper gave the application architecture of multienergy heat collection system functions, and developed a system model without losing simulation performance using stochastic modeling ideas. Second, the paper selected to make full use of the error information of the process, thus correcting the predicted value of remote temperature control output. According to the remote temperature control mode of multienergy heat collection proposed in this paper, this paper drew the outlet temperature change curve of water tank in all working conditions of the system. In addition, this paper collected the PID parameters of the thyristor controlling the system heat flow and the mean square error of inlet temperature of heat collection water tank in different experimental schemes. This paper gave the error values of measured and predicted temperature and theoretical temperature at different test nodes, which verified that the model in this paper obtained effective results in controlling the remote temperature of multienergy heat collection system. Then this paper further analyzed the variance of remote temperature control experiment of multienergy heat collection. The experimental results showed that the controlled multienergy heat collection system had rapid temperature response, short adjustment time, and relatively ideal control effect and robust performance.
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