District Heating And Cooling In The Desert: An Efficient Approach To Energy Management

District Heating And Cooling In The Desert: An Efficient Approach To Energy Management – Looking for an alternative to a new furnace or air conditioning system for your home, building, campus or community? Geothermal heating and cooling technologies are a great option.

Heat pumps move heat from one place to another using electricity. Air conditioners and refrigerators are two typical examples of heat pumps. Heat pumps can also be used to heat and cool buildings.

District Heating And Cooling In The Desert: An Efficient Approach To Energy Management

District Heating And Cooling In The Desert: An Efficient Approach To Energy Management

Temperatures about 30 feet below the surface remain relatively constant throughout the year, ranging from about 50°F (10°C) to 59°F (15°C). For most areas of the United States, this means that the soil temperature is usually warmer than the air in the winter and cooler than the air in the summer.

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Geothermal heat pumps (GHPs) use these constant underground temperatures to efficiently exchange temperatures, heating homes in the winter and cooling homes in the summer.

Geothermal heat pumps use the constant underground temperature of shallow water as a heat storage device, providing efficient heating and cooling. Systems may differ in collector type and connection used.

No, geothermal heat pumps (GHPs) are different from air source heat pumps. GHP systems exchange heat from the ground, while air source heat pumps exchange heat from the air.

Compared to air systems, geothermal systems have proven to be quieter, last longer and require less maintenance, and they are independent of outside air temperature. Geothermal systems are usually more expensive than air source systems, but the additional cost is often recouped in savings.

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Geothermal heat pumps can be expanded to meet the heating and cooling needs of an entire community in a single network, as shown in this figure (click for larger version). Other geothermal heating and cooling technologies, such as district heating, can also be used in a community system.

In April 2023, the US Department of Energy (DOE) announced selection for the Joint Geothermal Heating and Cooling Initiative and Deployment. Projects will receive funding to help communities develop and deploy geothermal district heating and cooling systems, create appropriate workforce training, and identify and address environmental justice issues. The initiative will also promote geothermal expansion at the local scale by supporting new systems and developing case studies for replication across the country. Effect of GFRP wrapping on the lateral performance of double shear lap joints in cross-laminated timber as part of timber bridges

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District Heating And Cooling In The Desert: An Efficient Approach To Energy Management

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Author: Kashif Irshad Kashif Irshad Scilit Preprints.org Google Scholar 1, 2, * , Md. Hasan Zahir Md. Hasan Zahir Scilit Preprints.org Google Scholar 1, Mahaboob Sharief Shaik Mahaboob Sharief Shaik Scilit Preprints.org Google Scholar 3 and Amjad Ali Amjad Ali Scilit Preprints.org Google Scholar 1

West Warms To Geothermal Energy As A Path To Clean Power Goals

Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Received: 16 August 2022 / Revised: 21 September 2022 / Accepted: 5 October 2022 / Published: 12 October 2022

(This article belongs to the special issue “Practical application of the predictive control model and other advanced control methods in the embedded environment”)

District Heating And Cooling In The Desert: An Efficient Approach To Energy Management

An important aspect of improving the energy efficiency of buildings is the effective use of load forecasting models for heating and cooling of buildings. Much research has been done in recent years to predict cooling and heating loads. The selection of the most effective input parameters, as well as the development of a forecasting model with high accuracy, are the most difficult and important aspects of forecasting. The purpose of this study is to create an intelligent data-based load forecasting model for residential heating and cooling load intensity. This paper presents a fuzzy reasoning-based self-systematized intelligent neural network (SSRD-SsIF-NN) coupled with red deer herding optimization as a new data-driven intelligent load forecasting method. A simulated dataset based on the climate of Dhahran, Saudi Arabia, with building system parameters as inputs and heating and cooling loads as outputs for each system, will be used to validate the proposed approaches. The simulation of this study was performed using MATLAB software. Finally, theoretical and experimental results demonstrate the effectiveness of the presented methods. In terms of mean square error (MSE), root mean square error (RMSE), regression values ​​(R), mean absolute error (MAE), coefficient of determination (R2) and other indicators, their forecasting performance is compared with the forecasting performance of other conventional methods. This shows that the proposed method achieved the best load forecasting performance compared to conventional methods.

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The share of residential structures has increased during the last ten years due to global concern about climate change, global carbon emissions, global warming, urbanization and rapid development of construction [1]. Many procedures and technologies in residential and commercial buildings serve to maintain the environment at a pleasant and favorable level, but they cost energy, which increases the heating and cooling load [2]. Many studies have been conducted on the energy profile of buildings, as well as many elements of effective development of buildings [3, 4]. In Saudi Arabia, many residential buildings are attached or semi-detached, which require more cooling and heating than conventional apartments [5]. Temperature, humidity, operation of lighting devices, elements of construction and design of buildings play a certain role in heating and cooling structures [6].

Material used for wall surfaces, relative compactness of building structures, window glazing area, ceiling dimensions, outer layer and density of the building, outer layer and wall density, roof height, number of wall surfaces. and their region, the orientation of the halls and the building, as well as the height of the tribune – all this is involved in the construction and related to the environment. However, several aspects of building design and layout have a significant impact on the building’s heating and cooling load, which directly affects the overall performance of the building [7]. Thus, by reducing the computational burden for optimal design that includes several subsets of building functions, accurate and fast prediction of space heating and cooling loads improves energy conservation and carbon mitigation [8]. Developing machine learning methods to predict heating and cooling needs can help improve efficiency and accuracy in real time [9]. Traditional algorithms for predictive modeling of heating and cooling include minimum likelihood regression (MPMR) [10], deep neural network (DNN) [11], Gaussian process regression (GPR) [12] and gradient boosting machine (GBM) [13]. In addition, Artificial Neural Networks (ANN) [14], Categorization and Regression Trees (CART) [15], General Linear Regression (GLR) [16] and Chi-Square Automated Interaction Detectors (CHAID) [17] have been used for prediction. building cooling and heating requirements.

As a result, various hybrid methodologies based on ANNs and metaheuristic schemes have been developed to predict the performance of building heating and cooling demand, including Imperialistic Competitive Algorithm (ICA) [18], Artificial Bee Colony (ABC) [19], Genetic Algorithm (GA) [ 20], whale optimization [21], bat optimization [22] and particle swarm optimization (PSO) [23]. However, such factors have been used in some studies with little benefit. In most of the previous academic studies, meteorological parameters were used as an indication and input data for estimating the cooling/heating needs of apartment buildings [24]. Environmental and climatic conditions do not affect the cooling/heating load of residential construction; that is for sure. However, sudden changes in weather can cause disruption of stable models, reducing the reliability factor and increasing errors in the process [25].

This study focuses on building construction and design characteristics and their impact on heating/cooling loads. In addition to the construction of controlled classification predictive models, the study applied in-depth testing of the structural features of the energy supply of buildings. The magnitude of the cooling/heating load was considered as an output parameter, although the collection of information on the structural attributes of the structure was considered as an input parameter. Below are the main research contributions:

Central Solar Heating

The rest of the essay is organized as follows. Definitions of investigative gaps are provided in Chapter 2 along with

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