The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the...The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.展开更多
This article introduces a string of energy conservation measures adopted over the past sev- eral years by the RFCC unit at Shengli Petrochemical Complex, including the optimization of feedstock properties, the adoptio...This article introduces a string of energy conservation measures adopted over the past sev- eral years by the RFCC unit at Shengli Petrochemical Complex, including the optimization of feedstock properties, the adoption of high-efficiency atomizing nozzles, the revamp of CO boiler, the atomization by means of dry gas, the post-burning of flue gas as well as the application of frequency converting machines and pumps, resulting in ideal effects. The energy consumption of the RFCC unit was gradually decreased to 2984.25 MJ/t from the original level of 3716.99 MJ/t. After comparing basic energy con- sumption values with actual consumption values, the authors have set forward measures for further energy conservation, such as the recovery of low-temperature excess heat contained in oil/gas streams exiting from the fractionation tower top, addition of the fourth cyclone, delivery of hot oil slurry, and heat tracing with hot water.展开更多
文摘The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.
文摘This article introduces a string of energy conservation measures adopted over the past sev- eral years by the RFCC unit at Shengli Petrochemical Complex, including the optimization of feedstock properties, the adoption of high-efficiency atomizing nozzles, the revamp of CO boiler, the atomization by means of dry gas, the post-burning of flue gas as well as the application of frequency converting machines and pumps, resulting in ideal effects. The energy consumption of the RFCC unit was gradually decreased to 2984.25 MJ/t from the original level of 3716.99 MJ/t. After comparing basic energy con- sumption values with actual consumption values, the authors have set forward measures for further energy conservation, such as the recovery of low-temperature excess heat contained in oil/gas streams exiting from the fractionation tower top, addition of the fourth cyclone, delivery of hot oil slurry, and heat tracing with hot water.
