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تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management
استقرار مدل های پیش بینی خنک کننده افقی کوتاه مدت و میان مدت برای داده کاوی برای بهینه سازی و مدیریت انرژی ساختمان-2019
In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are –multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg–Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is prac- ticed calculating the correlation between actual cooling load demand and input feature variables of cli- mate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation ( R ); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model’s performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited num- ber of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction
Keywords: Water source heat pump | Data mining models | Cooling load prediction | Building load
مقاله انگلیسی
2 Water source heat pump energy demand prognosticate using disparate data-mining based approaches
منبع آب پمپ حرارتی پیش بینی تقاضای انرژی با استفاده از روش های متفاوت بر اساس داده کاوی-2018
This paper examines the data-mining and supervised based machine learning models for predicting 1- month ahead cooling load demand of an office building, including the primitive intention of enhancing the forecasting performance and the accuracy. The data-mining and supervised based ma chine learning models include; regression support vector machine, Gaussian process regression, scaled conjugate gradient, tree bagger, boosted tree, bagged tree, neural network, multiple linear regression and bayesian regularization. The external climate data, hours/day in a week, previous week load, previous day load and previous 24-h average load are applied as input parameters for these models. Whereas, the output of the models is the electrical power required for water source heat pump. A water source heat pump located in Beijing, China, is selected for examining 1-month ahead cooling load forecasting, i.e., from July 8 to August 7, 2016. In this paper, simulations are classified into three sessions: 7-days, 14-days and 1-month. The forecast performance is assessed by computing four performance indices such as mean square error, mean absolute error, root mean square error and mean absolute percentage error. The mean absolute percentage error for 7-days ahead cooling load prediction of the water source heat pump from data-mining based models, Gaussian process regression, tree bagger, boosted tree, bagged tree and multiple linear regression were 0.405%, 3.544%, 1.928%, 1.703% and 13.053% respectively. While, mean absolute percentage error of 7-days ahead forecasting in case of machine learning based models such as a regression support vector machine, Bayesian regularization, scaled conjugate gradient and neural network were 12.761%, 2.314%, 6.314%, 2.592% respectively. The percentage forecasting error index proved that the results of data-mining based models are more precise and similar to the existing ma chine learning models. The results also demonstrate that the better performance and efficiency in foreseeing the abnormal behaviour in forecasting and future cooling load demand in the building environment.
Keywords: water source heat pump ، energy demand prediction ، Clustering analysis ، Data-mining
مقاله انگلیسی
3 Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches
پیش بینی کوتاه مدت و متوسط مدت تقاضای خنک کننده و گرمای بار در محیط ساختمان با رویکرد مبتنی بر داده کاوی-2018
This paper depicted the novel data mining based methods that consist of six models for predicting accu rate future heating and cooling load demand of water source heat pump, with the objective of enhancing the prediction accuracy and the management of future load. The proposed model was developed to ease generalization to other buildings, by making use of readily available measurements of a comparatively small number of variables related to water source heat pump operation in the building environment. The six models are - tree bagger, Gaussian process regression, multiple linear regression, bagged tree, boosted tree and neural network. The input parameter comprised the prescribed period, external climate data and the diverse load conditions of water source heat pump. The output was electrical power consump tion of water source heat pump. In this study, simulations were conducted in three sessions - 7-day, 14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models were measured by diverse indices. The performance indices which were used in assessing the prediction performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean square error, mean square error and mean absolute percentage error. The mean absolute percentage er ror results for 7-day future energy demand forecasting from tree bagger, Gaussian process regression, bagged tree, boosted tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%, 1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance was compared with the existing studies, the mean absolute percentage error of 2.515% was found out for the first session, 7-day. The results also showed that the six models were efficient in foreseeing the abnormal behavior and future cooling and heating load demand in the building environment.
Keywords: Data mining based approaches ، Water source heat pump ، Clustering Analysis ، Load forecasting
مقاله انگلیسی
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بازدید امروز: 2813 :::::::: بازدید دیروز: 3097 :::::::: بازدید کل: 37080 :::::::: افراد آنلاین: 46