scholarly journals Spatio-Temporal Analysis of Heating and Cooling Degree-Days Over Iran

2021 ◽  
Author(s):  
Amin Sadeqi ◽  
Hossein Tabari ◽  
Yagob Dinpashoh
Keyword(s):  
Time Series ◽  
Energy Demand ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Degree Days ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Spatio Temporal ◽  
Heating Degree Days ◽  
Cooling Energy Demand

Abstract Climate change affects the energy demand in different sectors of the society. To investigate this possible impact, in this research, temporal trends and change points in heating degree-days (HDD), cooling degree-days (CDD), and their simultaneous combination (HDD+CDD) were analysed for a 60-year period (1960-2019) in Iran. The results show that less than 20% of the study stations had significant trends (either upward or downward) in HDD time series, while more than 80% of the stations had significant increasing trends in CDD and HDD+CDD time series. Abrupt changes in HDD time series mostly occurred in the early 1980s, but those in CDD time series were mostly observed in the 1990s. The cooling energy demand in Iran has dramatically increased as CDD values have raised up from 690 ºC-days to 1010 ºC-days in the last 60 years. HDD, however, almost remained constant in the same period. The results suggest that if global warming continues with the current pace, cooling energy demand in the residential sector will considerably increase in the future, calling for a change in residential energy consumption policies.

scholarly journals Large uncertainties in trends of energy demand for heating and cooling under climate change

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Adrien Deroubaix ◽  
Inga Labuhn ◽  
Marie Camredon ◽  
Benjamin Gaubert ◽  
Paul-Arthur Monerie ◽  
...  
Keyword(s):  
Energy Demand ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Degree Days ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Climate Model Simulations ◽  
Increasing Trends ◽  
Cooling Energy Demand

AbstractThe energy demand for heating and cooling buildings is changing with global warming. Using proxies of climate-driven energy demand based on the heating and cooling Degree-Days methodology applied to thirty global climate model simulations, we show that, over all continental areas, the climate-driven energy demand trends for heating and cooling were weak, changing by less than 10% from 1950 to 1990, but become stronger from 1990 to 2030, changing by more than 10%. With the multi-model mean, the increasing trends in cooling energy demand are more pronounced than the decreasing trends in heating. The changes in cooling, however, are highly variable depending on individual simulations, ranging from a few to several hundred percent in most of the densely populated mid-latitude areas. This work presents an example of the challenges that accompany future energy demand quantification as a result of the uncertainty in the projected climate.

scholarly journals Heating and Cooling Degree-Days Climate Change Projections for Portugal

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Cristina Andrade ◽  
Sandra Mourato ◽  
João Ramos
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Residential Buildings ◽  
Degree Days ◽  
Climate Change Projections ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Heating Energy Demand

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating (HDD) and cooling (CDD) degree-days along with HDD + CDD were computed from an ensemble of seven high-resolution bias-corrected simulations attained from EURO-CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5). These three indicators were analyzed for 1971–2000 (from E-OBS) and 2011–2040, and 2041–2070, under both RCPs. Results predict a decrease in HDDs most significant under RCP8.5. Conversely, it is projected an increase of CDD values for both scenarios. The decrease in HDDs is projected to be higher than the increase in CDDs hinting to an increase in the energy demand to cool internal environments in Portugal. Statistically significant linear CDD trends were only found for 2041–2070 under RCP4.5. Towards 2070, higher(lower) CDD (HDD and HDD + CDD) anomaly amplitudes are depicted, mainly under RCP8.5. Within the five NUTS II

A method to estimate the heating and cooling degree-days for different climatic zones of Saudi Arabia

2016 ◽  
Vol 38 (3) ◽  
pp. 327-350 ◽  
Author(s):  
Madhavi Indraganti ◽  
Djamel Boussaa
Keyword(s):  
Saudi Arabia ◽  
Energy Consumption ◽  
Regression Models ◽  
Outdoor Environment ◽  
Base Temperature ◽  
Degree Days ◽  
Climatic Zones ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Heating Degree Days

Saudi Arabia’s energy consumption is increasing astronomically. Saudi Building Code prescribes a fixed base temperature of 18.3℃ to estimate the heating degree-days and cooling degree-days. Using historical meteorological data (2005–2014), this article presents the heating degree-days and cooling degree-days estimated for the representative cities in all the five inhabited climatic zones of Saudi Arabia. We used the base temperatures of 14℃, 16℃ and 18℃ for heating degree-days, and 18℃, 20℃, 22℃, 24℃ and 28℃ for cooling degree-days for Dhahran, Guriat, Jeddah, Khamis Mushait and Riyadh cities. We developed multiple regression models for heating degree-days and cooling degree-days at various base temperatures for these zones. Degree-days for other cities in similar climates with limited input data can be computed with these. Lowering of base temperature by 2 K from 18℃ reduced the heating degree-days by 33–65%. At 14℃ of base temperature, the heating requirement reduced by 60–95%. Elevating the base temperature by 2 K from 18℃ lowered the cooling degree-days by 16–38%. At 28℃ of base temperature cooling can be completely eliminated in Khamis Mushait, and reduced by 65–92% in other cities. This observation merits rethinking about use of appropriate base temperatures that properly link the outdoor environment to reduce the energy consumption. Practical application: Using historical data, we developed regression models for predicting heating and cooling degree-days for five cities of Saudi Arabia in various climate zones without the historic data. Using these, we can estimate the changes in heating/cooling load due to the variation in base temperatures. For example, lowering base temperature by 2–4 K from 18℃ reduces the HDDs by 33–95% and elevating the base temperature by 2–4 K from 18℃ lowered the CDDs by 16–68%.

scholarly journals The valuation of a natural gas-fired power plant with multiple turbines using clean spark spread and weather options

2021 ◽  
Author(s):  
M. Reaz-us Salam Elias
Keyword(s):  
Power Plant ◽  
Natural Gas ◽  
Regime Switching ◽  
Gas Turbines ◽  
Emission Rates ◽  
Degree Days ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Spread Option ◽  
Heating Degree Days

Assessing the value of a power plant is an important issue for plant owners and prospective buyers. In a deregulated market, an owner has the option to operate the plant when the revenue from selling the electricity is higher than the cost of operating the plant. This option is known as the spark spread option. Under emission restrictions, when the carbon cost is deducted from the spark spread, the option is named as the clean spark spread option. This thesis presents an analysis on the spark spread and clean spark spread option based valuation methods for a power plant with multiple gas turbines having different input–output characteristics, emission rates, and capacities. Electricity, natural gas and carbon allowance prices are assumed to follow mean–reverting processes. Results demonstrate that CO2 allowance cost reduces the expected plant value, while the flexibility of switching among turbines adds value to the power plant. Weather also affects the power plant operation. This thesis also presents a valuation model for a power plant integrating spark spread and weather options. A cooler winter drawing more electricity could generate a higher payoff for the plant owner. A warmer winter, however, could lead to a lower payoff. An owner holding a long position in a temperature–based put option could exercise the option when the winter is milder. The exercise is triggered by the drop of heating degree days below a strike degree day. The number of weather contracts to buy is determined by minimizing the variance of the total payoff. Pricing of the weather option is calculated based on the mean–reverting behavior of temperature. Results demonstrate that the integrating weather option along with spark spread option adds value to the downward spark spread option based valuation of the plant in a warmer winter. A comparison of temperature modeling approaches with an aim to pricing weather option is also investigated. Regime–switching models generated from a combination of different underlying processes are utilized to determine the expected heating and cooling degree days. Weather option prices are then calculated based on a range of strike heating degree days.

scholarly journals Heating and Cooling Degree-Days Climate Change Projections for Portugal

2021 ◽  
Author(s):  
Cristina Andrade ◽  
Sandra Mourato ◽  
João Ramos
Keyword(s):  
Climate Change ◽  
Energy Demand ◽  
Residential Buildings ◽  
Linear Trend ◽  
Degree Days ◽  
Climate Change Projections ◽  
Degree Day ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Heating Energy Demand

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating degree-day (HDD), the cooling degree-day (CDD) and the HDD+CDD were computed from an ensemble of 7 high-resolution bias-corrected simulations attained from EURO-CORDEX under RCP4.5 and RCP8.5. These three indicators were analyzed for 1971-2000 (from E-OBS) and 2011-2040 and 2041-2070, under both RCPs. Results show that the overall spatial distribution of HDD trends for the 3 time-periods points out an increase of energy demand to heat internal environments in Portugal's northern-eastern regions, most significant under RCP8.5. It is projected an increase of CDD values for both scenarios; however, statistically significant linear trends were only found for 2041-2070 under RCP4.5. The need for cooling is almost negligible for the remaining periods, though linear trend values are still considerably higher for 2041-2070 under RCP8.5. By the end of 2070, higher amplitudes for all indicators are depicted for southern Algarve and Alentejo regions, mainly under RCP8.5. For 2041-2070 the Centre and Alentejo (North and Centre) regions present major positive differences for HDD(CDD) under RCP4.5(RCP8.5), within the 5 NUTS II regions predicting higher heating(cooling) requirements for some locations.

scholarly journals The valuation of a natural gas-fired power plant with multiple turbines using clean spark spread and weather options

2021 ◽  
Author(s):  
M. Reaz-us Salam Elias
Keyword(s):  
Power Plant ◽  
Natural Gas ◽  
Regime Switching ◽  
Gas Turbines ◽  
Emission Rates ◽  
Degree Days ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Spread Option ◽  
Heating Degree Days

Assessing the value of a power plant is an important issue for plant owners and prospective buyers. In a deregulated market, an owner has the option to operate the plant when the revenue from selling the electricity is higher than the cost of operating the plant. This option is known as the spark spread option. Under emission restrictions, when the carbon cost is deducted from the spark spread, the option is named as the clean spark spread option. This thesis presents an analysis on the spark spread and clean spark spread option based valuation methods for a power plant with multiple gas turbines having different input–output characteristics, emission rates, and capacities. Electricity, natural gas and carbon allowance prices are assumed to follow mean–reverting processes. Results demonstrate that CO2 allowance cost reduces the expected plant value, while the flexibility of switching among turbines adds value to the power plant. Weather also affects the power plant operation. This thesis also presents a valuation model for a power plant integrating spark spread and weather options. A cooler winter drawing more electricity could generate a higher payoff for the plant owner. A warmer winter, however, could lead to a lower payoff. An owner holding a long position in a temperature–based put option could exercise the option when the winter is milder. The exercise is triggered by the drop of heating degree days below a strike degree day. The number of weather contracts to buy is determined by minimizing the variance of the total payoff. Pricing of the weather option is calculated based on the mean–reverting behavior of temperature. Results demonstrate that the integrating weather option along with spark spread option adds value to the downward spark spread option based valuation of the plant in a warmer winter. A comparison of temperature modeling approaches with an aim to pricing weather option is also investigated. Regime–switching models generated from a combination of different underlying processes are utilized to determine the expected heating and cooling degree days. Weather option prices are then calculated based on a range of strike heating degree days.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

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