Energy Consumption Prediction using Degree Days based on Comfort Temperature

Utkarsh,; Framewala, Aman; Natarajan, Muthukumaran

doi:10.1109/ficloud49777.2021.00026

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The heating degree days (HDD) metric can be used for that purpose [30], optionally in combination with metered energy use [31]. Using only HDD is not sufficient to estimate user comfort [32]. However, it is very useful in combination with information of the energy consumed by an HVAC system and information about indoor temperature.…”

Section: Literature Reviewmentioning

confidence: 99%

The Architecture for Testing Central Heating Control Algorithms with Feedback from Wireless Temperature Sensors

Markiewicz,

Skała,

Grela

et al. 2023

Energies

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The energy consumption of buildings is a significant contributor to overall energy consumption in developed countries. Therefore, there is great demand for intelligent buildings in which energy consumption is optimized. Online control is a crucial aspect of such optimization. The implementation of modern algorithms that take advantage of developments in information technology, artificial intelligence, machine learning, sensors, and the Internet of Things (IoT) is used in this context. In this paper, an architecture for testing central heating control algorithms as well as the control algorithms of the heating system of the building is presented. In particular, evaluation metrics, the method for seamless integration, and the mechanism for real-time performance monitoring and control are put forward. The proposed tools have been successfully tested in a residential building, and the conducted tests confirmed the efficiency of the proposed solution.

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Section: Literature Reviewmentioning

confidence: 99%

The Architecture for Testing Central Heating Control Algorithms with Feedback from Wireless Temperature Sensors

Markiewicz,

Skała,

Grela

et al. 2023

Energies

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“…( 5) [17] ………………... (6) [17] Errors may be seen due to inappropriate value of σ and they may increase if Tmm and Tb are close. [14], [17], [29], [31]Base temperature here will also be taken 24⁰C. [32]Calculations are done below by Hitchins method for each city mentioned.…”

Section: American Society Of Heating Refrigerating and Air-conditioni...mentioning

confidence: 99%

Forecasting Energy Consumption Using Calculation of CDDs in Pakistan Using Two Different Methods

Soomro,

Memon,

Soomro

2022

JAET

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Energy consumption has increased due to industrialization and other factors involved which gives the researchers to ponder upon the methods to set policy and forecast the need of energy in coming days. Pakistan with different cities comprising of various climatic zones, this study shown the highest CDDs in city of Hyderabad among large cities with high population flux and then followed by Karachi with 2nd highest CDDs among the mentioned cities with highest population and thus huge consumption of power, Quetta having lowest CDDs among the cities due to highland and cold climatic region. Pakistan location is affected by the GHGs aftermath is floods and different natural catastrophe as mentioned by UN because it is surrounded one of largest emitters of CO2 and Pakistan is 17th Largest emitter of GHGs. These natural catastrophes can be due to coal power projects emissions that are increasing day by day and effecting the local climate of Pakistan, but the neighboring countries emissions is also affecting the climate of Pakistan. There are two methods discussed in this paper and their results and validation are thus observed. The two methods are ASHRAEs method and Hitchens’s method. The aim is to calculate the CDDs of major cities or provincial capitals of Pakistan. The study will help policy makers to plan the power projects accordingly by considering the degree days in major cities of Pakistan. In this research climatic regions of the country were studied with the average temperatures taken for the calculation of CDDs of major cities of Pakistan and their data was procured from meteorological department of Pakistan to study the CDDs of the few cities. This is mostly used for prediction of monthly degree days using Hitchens’s method for month of year and can be conveyed to power generation and distribution companies. Hitchens’s method and ASHRAE were used, and it validated the calculation for every mentioned city in Pakistan. Due to simplicity ASHRAE came out as simple and balanced one and authenticated by most researchers worldwide.

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“…Additionally, the authors stress the importance of considering outdoor temperature in their analysis, suggesting the use of heating degree days (HDD) as a metric for this purpose [24], possibly in conjunction with metered energy usage data [25]. However, relying solely on HDD is insufficient for assessing user comfort [26], but it becomes valuable when combined with information about HVAC energy consumption and indoor temperature. Other performance criteria can also be employed as optimization objectives in various HVAC control strategies.…”

mentioning

confidence: 99%

Implementation of Building a Thermal Model to Improve Energy Efficiency of the Central Heating System—A Case Study

Skała,

Grela,

Latoń

et al. 2023

Energies

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This paper presents the concept of an innovative control of a central heating system in a multifamily building based on the original thermodynamic model, the resulting architecture of the control system, and the originally designed and manufactured wireless temperature sensors for thermal zones. The novelty of this solution is the developed layers of the control system: distributed measurement and correction analysis, which is based on the existing infrastructure and the local HVAC controller. This approach allows for the effective use of the measured temperature data from thermal zones and finally sending the value of the calculated correction of settings to the controller. Moreover, in the analytical layer, a model was also implemented that calculates the necessary amount of energy based on data from the subsystem of temperature sensors located in the thermal zones of the building. The use of the algorithmic strategy presented in this paper extends the functionality and significantly improves the energy efficiency of the existing, classic, reference heating control algorithm by implementing additional control loops. Additionally, it enables integration with demand-side response systems. The presented concept was successfully tested, achieving real energy savings for heating by 12%. These results are described in a case-study format. The authors believe that this concept can be used in other buildings and thus will have a positive impact on the energy savings used to maintain thermal comfort in buildings and significantly reduce CO2 emissions.

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Energy Consumption Prediction using Degree Days based on Comfort Temperature

Cited by 3 publications

References 16 publications

The Architecture for Testing Central Heating Control Algorithms with Feedback from Wireless Temperature Sensors

The Architecture for Testing Central Heating Control Algorithms with Feedback from Wireless Temperature Sensors

Forecasting Energy Consumption Using Calculation of CDDs in Pakistan Using Two Different Methods

Implementation of Building a Thermal Model to Improve Energy Efficiency of the Central Heating System—A Case Study

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