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
The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,...The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,it is crucial to predict future energy demand. Electricity demandforecasting is difficult because of the complexity of the available demandpatterns. Establishing a perfect prediction of energy consumption at thebuilding’s level is vital and significant to efficiently managing the consumedenergy by utilising a strong predictive model. Low forecast accuracy is justone of the reasons why energy consumption and prediction models havefailed to advance. Therefore, the purpose of this study is to create an IoTbasedenergy prediction (IoT-EP) model that can reliably estimate the energyconsumption of smart buildings. A real-world test case on power predictionsis conducted on a local electricity grid to test the practicality of the approach.The proposed (IoT-EP) model selects the significant features as input neurons,the predictable data is selected as output nodes, and a multi-layer perceptronis constructed along with the features of the Convolution Neural Network(CNN) algorithm. The analysis of the proposed IoT-EP model has higheraccuracy of 90%, correlation of 89%, and variance of 16% in less training timeof 29.2 s, and with a higher prediction speed of 396 (observation/sec). Whencompared to existing models, the results showed that the proposed (IoT-EP)model outperforms with a satisfactory level of accuracy in predicting energyconsumption in smart buildings.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The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,it is crucial to predict future energy demand. Electricity demandforecasting is difficult because of the complexity of the available demandpatterns. Establishing a perfect prediction of energy consumption at thebuilding’s level is vital and significant to efficiently managing the consumedenergy by utilising a strong predictive model. Low forecast accuracy is justone of the reasons why energy consumption and prediction models havefailed to advance. Therefore, the purpose of this study is to create an IoTbasedenergy prediction (IoT-EP) model that can reliably estimate the energyconsumption of smart buildings. A real-world test case on power predictionsis conducted on a local electricity grid to test the practicality of the approach.The proposed (IoT-EP) model selects the significant features as input neurons,the predictable data is selected as output nodes, and a multi-layer perceptronis constructed along with the features of the Convolution Neural Network(CNN) algorithm. The analysis of the proposed IoT-EP model has higheraccuracy of 90%, correlation of 89%, and variance of 16% in less training timeof 29.2 s, and with a higher prediction speed of 396 (observation/sec). Whencompared to existing models, the results showed that the proposed (IoT-EP)model outperforms with a satisfactory level of accuracy in predicting energyconsumption in smart buildings.
