Enhanced Grey Prediction Model (IGM (1,1)) for Accurate Geothermal Heat Pump Stations Output Temperature Forecasting | Chapter 8 | Geography, Earth Science and Environment: Research Highlights Vol. 7

Recently, many developed countries have turned to the use of Geothermal Heat Pump Systems (GHPS), an alternative source of renewable energy that is essentially clean, flexible, and freely available. GHPS is environmentally friendly and once installed, either vertically or horizontally, it does not require additional gas for operation. This chapter presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the current predicted value by subtracting the error between the previous predicted value and the previous immediate mean of the measured value. Subsequently, the IGM (1,1) model was applied to predict the output temperature of the GHPSs at Oklahoma University, the University Politècnica de València, and Oakland University, respectively. For each GHPS, the model uses a small dataset of 24 data points (i.e., 24 h) for training to predict the output temperature eight hours in advance. To evaluate the prediction performance of the improved model, the Root Mean Square Error (RSME), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were used to measure the prediction accuracy of the IGM (1,1) model. The proposed model was verified using three different output temperature datasets; these datasets were also used to validate the power efficiency of the proposed model. In addition, the empirical results show that the proposed IGM (1,1) model significantly improves the simulation (in-sample) and the prediction (out-of-sample) of the output temperature of the GHPS through error reduction, thereby enhancing the GM (1,1) model’s overall accuracy. As a result, the prediction accuracies were compared, and the improved model was found to be more accurate than the GM (1,1) model in both simulation and prediction results for all datasets used. In the future, improvements are anticipated for the IGM (1,1) to make it even more accurate for geothermal heat pump systems for longer than just the short term.

 

Author (s) Details

Khaled Salhein
Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA.

 

Javed Ashraf
Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA.

 

Mohamed Zohdy
Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/geserh/v7/4656

Determining the Relationship between Coronavirus and Stock Market Volatility in Emerging Countries | Chapter 7 | Current Aspects in Business, Economics and Finance Vol.5

 The present study optimize the cost of dairy products in manufacturing in a dairy factory so that consumers should get advantaged from the price of separate product deals in the request. also, this study helps to reduce the running and functional losses of the dairy factory. The study was accepted in the Experimental Dairy Factory, public Dairy Research Institute, Karnal( Haryana), with a running capacity of ten thousand liters per day, to optimize the cost of Dairy products manufactured in the dairy factory. Burfi and Ice- cream are among the most pivotal Indian dairy products reused in a dairy factory and extensively consumed. Primary and Secondary data were both used to conduct this study. Primary compliances and interviews with factory workers round out the primary data. Secondary data, similar as milk flux, its operation pattern, and product affair, were gathered from colorful factory checks. To reduce the cost of each component used to make Burfi and ice cream, an profitable study of this dairy product in a dairy factory is needed. The dairy product will keep its presence in the competitive request, and as a result, the consumer will profit from this optimum price. thus, calculated the product cost was in a dairy factory, and the fixed and variable cost was24.56 and75.44 percent for Burfi and34.01 and65.99 percent for ice cream, independently. This study shows that the dairy factory has fat product of3721.69 kg and mugs of 100 ml of affair Burfi and Ice- cream, independently, after the break-even point. This study suggests that the small milk patron and tone- help groups should engage in value- added product processing on their own by taking training from institutions to induce income and employment to support the pastoral frugality.                                         

Author(s) Details:

Muhammad Mar’I,
Department of Banking and Finance, Near East University, North Cyprus, Mersin 10, Turkey.

Turgut Tursoy,
Department of Banking and Finance, Near East University, North Cyprus, Mersin 10, Turkey.

Please see the link here: https://stm.bookpi.org/CABEF-V5/article/view/8454