植物工厂地源热泵系统热负荷BP神经网络预测及验证
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摘要
为提高水蓄能型地下水源热泵自然光植物工厂供热系统节能性,供热系统必须能够很好地预测热负荷变化。针对自然光植物工厂热环境系统非线性特点,利用具有很强非线性映射能力的BP神经网络(back propagation,BP),选取室内外空气干球温度、太阳辐射强度、室内相对湿度和绝对湿度、室内风速等输入参数,确定算法步骤和评价指标,构建神经网络模型预测植物工厂次日负荷。采用Matlab神经网络工具箱对崇明试验基地水蓄能型地源热泵自然光植物工厂的样本集进行训练,训练后误差函数值为0.002 999 94,神经网络收敛。通过对比热负荷预测值与实际值,证明了神经网络预测热负荷值与实际值趋势一致,基本误差在±6%以内,结果表明神经网络法可以用于植物工厂次日热负荷预测。通过热负荷预测能够更加科学地调整供热系统运行模式,更好地匹配植物工厂需求热量与热泵的输出能量,实现运行节能和降低供能成本的目的。
It is important for crop growth to maintain suitable temperature in plant factory, however large heating energy consumption has been proved to be an obstacle that restricts its development. In Europe, the cost of heating accounts for about 30% of the total operation cost during the winter, but in the north of latitude 43° of China, the proportion reaches 60% to 70%. The traditional heating equipment such as coal-fired boilers has an energy utilization rate of only 40% to 50%. So it is very necessary to apply renewable energy to plant factory. Regulating heating modes by tracking and predicting the heating load changes in plant factory is the key to achieve energy saving. Because of the high energy consumption in winter, accurate heating load prediction can improve the energy saving effect of groundwater source heat pump with water energy storage. Changes of heating load in natural light plant factory are dynamic, time-varying, highly turbulent and uncertain. Artificial neural networks is ideal for predicting load changes, especially BP(back propagation) neural network has strong nonlinear mapping ability, which is generally used by many scholars for building heating load prediction, but rarely in plant factory. Given that heating load of both plant factory and building have nonlinear characteristics, we used BP neural network to predict the next day's heating load of plant factory to promote energy-saving control optimization. The BP neural network model has three levels: input layer, hidden layer and output layer. Input parameters include indoor and outdoor air temperature, solar radiation intensity, indoor relative humidity, indoor absolute humidity, indoor wind speed, etc. For plant factory, the next day's weather condition has a significant impact on the heating load. The output variable is determined as the next day's hourly glass greenhouse load value. The number of neurons in the input layer was 9, the number of neurons in the hidden layer was 13, the selected layer number of hidden layers was 1, the learning rate was 0.25 to 0.30, and the initial momentum factor was 0.9. Common evaluation indicators used to determine whether the neural network converges, included standard deviation, coefficient of variation, and expected error percentage. After algorithm steps being determined, the next day's heating load was predicted based on reasonable algorithmic procedures and steps. Experimental data in the paper was obtained from a natural light plant factory powered by groundwater source heat pump with water energy storage system in Chongming National Facility Agricultural Engineering Technology Research Center. Using the neural network toolbox of Matlab to train and simulate the model to process the experimental data from January 19 th to 28 th, the value of the error function was 0.002 999 94 which was less than the set value of 0.003, so the neural network was convergent. Prediction effect can be drawn by comparison between the actual surveyed value and the predicted value of the heating load. The main heating load was concentrated on 0:00-6:00 and 17:00-24:00 o'clock in the plant factory, and most of these periods were in the cheap electricity price period of Shanghai. Adjusting the operating strategy and operating mode of the energy supply system were based on the predicted heating load,the heat pump operated at full load or high load during the period of cheap electricity prices, and excess heat was stored in the hot storage tank. The hot storage tank provided heat to plant factory during the period of moderate and expensive electricity price. In this case, the energy cost would be reduced. Therefore, it was significantly economical to control start-stop time of the groundwater source heat pump with water energy storage for plant factory heating project. The error was controlled within ±6% basically between the actual value and the predicted value of the heating loads. Therefore, the results showed that the BP neural network was suitable for the next day's heating load prediction of plant factory.
It is important for crop growth to maintain suitable temperature in plant factory, however large heating energy consumption has been proved to be an obstacle that restricts its development. In Europe, the cost of heating accounts for about 30% of the total operation cost during the winter, but in the north of latitude 43° of China, the proportion reaches 60% to 70%. The traditional heating equipment such as coal-fired boilers has an energy utilization rate of only 40% to 50%. So it is very necessary to apply renewable energy to plant factory. Regulating heating modes by tracking and predicting the heating load changes in plant factory is the key to achieve energy saving. Because of the high energy consumption in winter, accurate heating load prediction can improve the energy saving effect of groundwater source heat pump with water energy storage. Changes of heating load in natural light plant factory are dynamic, time-varying, highly turbulent and uncertain. Artificial neural networks is ideal for predicting load changes, especially BP(back propagation) neural network has strong nonlinear mapping ability, which is generally used by many scholars for building heating load prediction, but rarely in plant factory. Given that heating load of both plant factory and building have nonlinear characteristics, we used BP neural network to predict the next day's heating load of plant factory to promote energy-saving control optimization. The BP neural network model has three levels: input layer, hidden layer and output layer. Input parameters include indoor and outdoor air temperature, solar radiation intensity, indoor relative humidity, indoor absolute humidity, indoor wind speed, etc. For plant factory, the next day's weather condition has a significant impact on the heating load. The output variable is determined as the next day's hourly glass greenhouse load value. The number of neurons in the input layer was 9, the number of neurons in the hidden layer was 13, the selected layer number of hidden layers was 1, the learning rate was 0.25 to 0.30, and the initial momentum factor was 0.9. Common evaluation indicators used to determine whether the neural network converges, included standard deviation, coefficient of variation, and expected error percentage. After algorithm steps being determined, the next day's heating load was predicted based on reasonable algorithmic procedures and steps. Experimental data in the paper was obtained from a natural light plant factory powered by groundwater source heat pump with water energy storage system in Chongming National Facility Agricultural Engineering Technology Research Center. Using the neural network toolbox of Matlab to train and simulate the model to process the experimental data from January 19 th to 28 th, the value of the error function was 0.002 999 94 which was less than the set value of 0.003, so the neural network was convergent. Prediction effect can be drawn by comparison between the actual surveyed value and the predicted value of the heating load. The main heating load was concentrated on 0:00-6:00 and 17:00-24:00 o'clock in the plant factory, and most of these periods were in the cheap electricity price period of Shanghai. Adjusting the operating strategy and operating mode of the energy supply system were based on the predicted heating load,the heat pump operated at full load or high load during the period of cheap electricity prices, and excess heat was stored in the hot storage tank. The hot storage tank provided heat to plant factory during the period of moderate and expensive electricity price. In this case, the energy cost would be reduced. Therefore, it was significantly economical to control start-stop time of the groundwater source heat pump with water energy storage for plant factory heating project. The error was controlled within ±6% basically between the actual value and the predicted value of the heating loads. Therefore, the results showed that the BP neural network was suitable for the next day's heating load prediction of plant factory.
引文
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[11]Vogler-Finck P J C,Bacher P,Madsen H.Online short-term forecast of greenhouse heat load using a weather forecast service[J].Applied Energy,2017,205:1298-1310.
[12]Ahamed M S,Guo H,Tanino K.A quasi-steady state model for predicting the heating requirements of conventional greenhouses in cold regions[J].Information Processing in Agriculture,2017,5(1):33-46.
[13]程秀花,毛罕平,伍德林,等.玻璃温室自然通风热环境时空分布数值模拟[J].农业机械学报,2009,40(6):179-183.Cheng Xiuhua,Mao Hanping,Wu Delin,et al.Numerical simulation of thermal profiles in spatial and temporal field for natural ventilated glasshouse[J].Transactions of the Chinese Society of Agricultural Machinery,2009,40(6):179-183.(in Chinese with English abstract)
[14]He Fen,Ma Chengwei.Modeling greenhouse air humidity by means of artificial neural network and principal component analysis[J].Computers and Electronics in Agriculture,2010,71(1):19-23.
[15]Wang H,Sánchez-Molina J A,Li M,et al.Leaf area index estimation for a greenhouse transpiration model using external climate conditions based on genetics algorithms,back-propagation neural networks and nonlinear autoregressive exogenous models[J].Agricultural Water Management,2017,183:107-115.
[16]陈教料,胥芳,张立彬,等.基于CFD技术的玻璃温室加热环境数值模拟[J].农业机械学报,2008,39(8):114-118.Chen Jiaoliao,Xu Fang,Zhang Libin,et al.CFO-based Simulation of temperature distribution in glass greenhouse with forced-air heater[J].Transactions of the Chinese Society of Agricultural Machinery,2008,39(8):114-118.(in Chinese with English abstract)
[17]Kumar R,Aggarwal R K,Sharma J D.Energy analysis of a building using artificial neural network:A review[J].Energy&Buildings,2013,65(4):352-358.
[18]Panigrahi S,Behera H S.A hybrid ETS-ANN model for time series forecasting[J].Engineering Applications of Artificial Intelligence,2017,66:49-59.
[19]Chou J S,Bui D K.Modeling heating and cooling loads by artific-ial intelligence for energy-efficient building design[J].Energy and Buildings,2014,82:437-446.
[20]Meng Anbo,Ge Jiafei,Yin Hao,et al.Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm[J].Energy Conversion&Management,2016,114:75-88.
[21]Guo Zhenhai,Wu Jie,Lu Haiyan,et al.A case study on a hybrid wind speed forecasting method using BP neural network[J].Knowledge-Based Systems,2011,24(7):1048-1056.
[22]代爱妮,周晓光,刘相东,等.基于BP神经网络的旁热式辐射与对流粮食干燥过程模型[J].农业机械学报,2017,48(3):351-360.Dai Aini,Zhou Xiaoguang,Liu Xiangdong,et al.Model of drying process for combined side-heat infrared radiation and convection grain dryer based on BP neural network[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(3):351-360.(in Chinese with English abstract)
[23]Wang Lan,Lee E W M,Yuen R K K.Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach[J].Applied Energy,2018,228:1740-1753.
[24]Ahmad T,Chen Huanxin.Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches[J].Energy&Buildings,2018,166:460-476.
[25]Castaeda-Miranda A,Castao V M.Smart frost control in greenhouses by neural networks models[J].Computers&Electronics in Agriculture,2017,137:102-114.
[26]Taki M,Ajabshirchi Y,Ranjbar S F,et al.Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse[J].Energy&Buildings,2016,110:314-329.
[27]张经博,郭凌,王朝霞,等.基于遗传算法优化BP神经网络的供暖系统热负荷预测模型[J].兵器装备工程学报,2014,35(4):152-156.Zhang Jingbo,Guo Ling,Wang Chaoxia,et al.Thermal load forecasting model of heating system based on genetic algorithm optimization BP neural network[J].Journal of Ordnance Equipment Engineering,2014,35(4):152-156.(in Chinese with English abstract)
[28]陈晓冉.水蓄冷空调系统负荷预测方法的研究[D].天津:天津大学,2009.Chen Xiaoran.Research of Cooling Load Prediction Method in Water Storage Air Conditioning System[D].Tianjin:Tianjin University,2009.(in Chinese with English abstract)
[29]Xie Ling.The heat load prediction model based on BP neural network-markov model[J].Procedia Computer Science,2017,107:296-300.
[30]孙敬.基于遗传神经网络的空调房间送风量预测研究[D].南京:南京工业大学,2006.Sun Jing.Study on Air Supply Volume Prediction of Air-Condition Room based on GA-ANN[D].Nanjing:Nanjing University of Technology,2006.(in Chinese with English abstract)
[31]Ren Guanghua,Cao Yuting,Wen Shiping,et al.A modified elman neural network with a new learning rate scheme[J].Neurocomputing,2018,286:11-18.
[32]Takase T,Oyama S,Kurihara M.Effective neural network training with adaptive learning rate based on training loss[J].Neural Networks,2018,DOI:10.1016/j.neunet.2018.01.016.
[33]田野.基于动量因子的神经网络群电流负荷预测模型[J].电力系统保护与控制,2016,44(17):31-38.Tian Ye.A forecasting model for current load of neural network group based upon momentum factor[J].Power System Protection and Control,2016,44(17):31-38.(in Chinese with English abstract)
[2]Bargach M N,Dahman A S,Boukallouch M.A heating system using flat plate collectors to improve the inside greenhouse microclimate in Morocco[J].Renewable Energy,1999,18(3):367-381.
[3]Taghavifar H,Mardani A.Prognostication of energy consumption and greenhouse gas(GHG)emissions analysis of apple production in West Azarbayjan of Iran using artificial neural network[J].Journal of Cleaner Production,2015,87(1):159-167.
[4]江亿,彭琛,燕达.中国建筑节能的技术路线图[J].建设科技,2012(17):12-19.
[5]Lim J H,Song J H,Song S Y.Development of operational guidelines for thermally activated building system according to heating and cooling load characteristics[J].Applied Energy,2014,126(126):123-135.
[6]González P A,Zamarre?o J M.Prediction of hourly energy consumption in buildings based on a feedback artificial neural network[J].Energy&Buildings,2005,37(6):595-601.
[7]Briana S,Gerrit H,Ronaldw M C.Artificial neural networks for automated year-round temperature prediction[J].Computers and Electronics in Agriculture,2009,68(1):52-61.
[8]石惠娴,任亦可,孟祥真,等.植物工厂水蓄能型地下水源热泵供热系统节能运行特性研究[J].农业工程学报,2018,34(23):157-163.Shi Huixian,Ren Yike,Meng Xiangzhen,et al.Research on energy-saving operating characteristics of water storage groundwater source heat pump heating system in plant factory[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(23):157-163.(in Chinese with English abstract)
[9]沈兴来.能源系统中电、气、冷、热负荷预测综述[J].电子世界,2017(11):28-28.
[10]张琦,谢慕君,贾其臣,等.热负荷预测中最有影响力参数测定[J].长春工业大学学报,2017,38(2):150-154.Zhang Qi,Xie Mujun,Jia Qichen,et al.Heat load forecasting input variable selection[J].Journal of Changchun University of Technology,2017,38(2):150-154.(in Chinese with English abstract)
[11]Vogler-Finck P J C,Bacher P,Madsen H.Online short-term forecast of greenhouse heat load using a weather forecast service[J].Applied Energy,2017,205:1298-1310.
[12]Ahamed M S,Guo H,Tanino K.A quasi-steady state model for predicting the heating requirements of conventional greenhouses in cold regions[J].Information Processing in Agriculture,2017,5(1):33-46.
[13]程秀花,毛罕平,伍德林,等.玻璃温室自然通风热环境时空分布数值模拟[J].农业机械学报,2009,40(6):179-183.Cheng Xiuhua,Mao Hanping,Wu Delin,et al.Numerical simulation of thermal profiles in spatial and temporal field for natural ventilated glasshouse[J].Transactions of the Chinese Society of Agricultural Machinery,2009,40(6):179-183.(in Chinese with English abstract)
[14]He Fen,Ma Chengwei.Modeling greenhouse air humidity by means of artificial neural network and principal component analysis[J].Computers and Electronics in Agriculture,2010,71(1):19-23.
[15]Wang H,Sánchez-Molina J A,Li M,et al.Leaf area index estimation for a greenhouse transpiration model using external climate conditions based on genetics algorithms,back-propagation neural networks and nonlinear autoregressive exogenous models[J].Agricultural Water Management,2017,183:107-115.
[16]陈教料,胥芳,张立彬,等.基于CFD技术的玻璃温室加热环境数值模拟[J].农业机械学报,2008,39(8):114-118.Chen Jiaoliao,Xu Fang,Zhang Libin,et al.CFO-based Simulation of temperature distribution in glass greenhouse with forced-air heater[J].Transactions of the Chinese Society of Agricultural Machinery,2008,39(8):114-118.(in Chinese with English abstract)
[17]Kumar R,Aggarwal R K,Sharma J D.Energy analysis of a building using artificial neural network:A review[J].Energy&Buildings,2013,65(4):352-358.
[18]Panigrahi S,Behera H S.A hybrid ETS-ANN model for time series forecasting[J].Engineering Applications of Artificial Intelligence,2017,66:49-59.
[19]Chou J S,Bui D K.Modeling heating and cooling loads by artific-ial intelligence for energy-efficient building design[J].Energy and Buildings,2014,82:437-446.
[20]Meng Anbo,Ge Jiafei,Yin Hao,et al.Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm[J].Energy Conversion&Management,2016,114:75-88.
[21]Guo Zhenhai,Wu Jie,Lu Haiyan,et al.A case study on a hybrid wind speed forecasting method using BP neural network[J].Knowledge-Based Systems,2011,24(7):1048-1056.
[22]代爱妮,周晓光,刘相东,等.基于BP神经网络的旁热式辐射与对流粮食干燥过程模型[J].农业机械学报,2017,48(3):351-360.Dai Aini,Zhou Xiaoguang,Liu Xiangdong,et al.Model of drying process for combined side-heat infrared radiation and convection grain dryer based on BP neural network[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(3):351-360.(in Chinese with English abstract)
[23]Wang Lan,Lee E W M,Yuen R K K.Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach[J].Applied Energy,2018,228:1740-1753.
[24]Ahmad T,Chen Huanxin.Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches[J].Energy&Buildings,2018,166:460-476.
[25]Castaeda-Miranda A,Castao V M.Smart frost control in greenhouses by neural networks models[J].Computers&Electronics in Agriculture,2017,137:102-114.
[26]Taki M,Ajabshirchi Y,Ranjbar S F,et al.Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse[J].Energy&Buildings,2016,110:314-329.
[27]张经博,郭凌,王朝霞,等.基于遗传算法优化BP神经网络的供暖系统热负荷预测模型[J].兵器装备工程学报,2014,35(4):152-156.Zhang Jingbo,Guo Ling,Wang Chaoxia,et al.Thermal load forecasting model of heating system based on genetic algorithm optimization BP neural network[J].Journal of Ordnance Equipment Engineering,2014,35(4):152-156.(in Chinese with English abstract)
[28]陈晓冉.水蓄冷空调系统负荷预测方法的研究[D].天津:天津大学,2009.Chen Xiaoran.Research of Cooling Load Prediction Method in Water Storage Air Conditioning System[D].Tianjin:Tianjin University,2009.(in Chinese with English abstract)
[29]Xie Ling.The heat load prediction model based on BP neural network-markov model[J].Procedia Computer Science,2017,107:296-300.
[30]孙敬.基于遗传神经网络的空调房间送风量预测研究[D].南京:南京工业大学,2006.Sun Jing.Study on Air Supply Volume Prediction of Air-Condition Room based on GA-ANN[D].Nanjing:Nanjing University of Technology,2006.(in Chinese with English abstract)
[31]Ren Guanghua,Cao Yuting,Wen Shiping,et al.A modified elman neural network with a new learning rate scheme[J].Neurocomputing,2018,286:11-18.
[32]Takase T,Oyama S,Kurihara M.Effective neural network training with adaptive learning rate based on training loss[J].Neural Networks,2018,DOI:10.1016/j.neunet.2018.01.016.
[33]田野.基于动量因子的神经网络群电流负荷预测模型[J].电力系统保护与控制,2016,44(17):31-38.Tian Ye.A forecasting model for current load of neural network group based upon momentum factor[J].Power System Protection and Control,2016,44(17):31-38.(in Chinese with English abstract)