A National Heat Demand Model for Germany

2017 ◽  
pp. 172-188
Author(s):  
Marcelo Esteban Muñoz Hidalgo
Keyword(s):  
Demand Model ◽  
Heat Demand

scholarly journals City Scale Demand Side Management in Three Different-Sized District Heating Systems

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3370 ◽  
Author(s):  
Kaisa Kontu ◽  
Jussi Vimpari ◽  
Petri Penttinen ◽  
Seppo Junnila
Keyword(s):  
Control Strategies ◽  
District Heating ◽  
Cost Savings ◽  
Demand Side Management ◽  
Management Control ◽  
Demand Side ◽  
Demand Model ◽  
Management Scenarios ◽  
Annual Peak ◽  
Heat Demand

Demand side management can add flexibility to a district heating (DH) system by balancing the customer’s hourly fluctuating heat demand. The aim of this study is to analyze how different demand side management control strategies, implemented into different customer segments, impact DH production. A city scale heat demand model is constructed from the hourly heat consumption data of different customer segments. This model is used to build several demand side management scenarios to examine the effect of them on both, the heat producer, and the customers. The simulations are run for three different-sized DH systems, representing typical DH systems in Finland, in order to understand how the demand side management implementations affect the production. The findings imply that the demand side management strategy must be built individually for each specific DH system; the changing consumption profiles of different customer segments should be taken into consideration. The results show that the value of demand side management for a DH companies remains low (less than 2% in cost savings), having an effect mostly upon the medium loads without any significant decrease in annual peak heat loads. Also, the findings reflect that the DH pricing models should be developed to make demand side management more attractive to DH customers.

scholarly journals Heat demand model for district heating simulation

2018 ◽  
Vol 210 ◽  
pp. 02044 ◽  
Author(s):  
Viliam Dolinay ◽  
Lubomir Vasek ◽  
Jakub Novak ◽  
Petr Chalupa ◽  
Erik Kral
Keyword(s):  
Control Systems ◽  
New Technology ◽  
Optimal Solution ◽  
District Heating ◽  
Heating System ◽  
Distribution Model ◽  
Demand Model ◽  
District Heating System ◽  
Heat Demand ◽  
Smart Control

Demand for affordable and sustainable energy is growing. Even though the technology of construction and insulation of buildings is continuously improving, heating is still a significant issue for large part of Europe. Building modern heating systems as well as upgrading existing ones requires incorporating new technology and smart control systems with sophisticated control algorithms. An essential part of the control systems are models that allow the simulation to verify proposed actions or use series of simulation experiments to find the optimal solution. Several simulation tools are specializing in the field of energy already, and some general tools can also be used. This article shows two methods of own prediction mechanism of the heat demand of individual consumers (buildings). Modelling of individual buildings is the basis of the simulation model of district heating which is being developed. The fundamental idea is to build a modular model for specific district heating and start from the endpoints - from the individual consumption objects that will be interconnected through the distribution model with other parts of district heating system such as other consumers and producers. It is assumed that the heat demand is the most challenging part of the prediction, and therefore the accuracy and quality of these models will be the most significant to the accuracy of the entire future result.

scholarly journals Heat demand forecasting in District Heating Network using XGBoost algorithm

2021 ◽  
Vol 323 ◽  
pp. 00004
Author(s):  
Maciej Bujalski ◽  
Paweł Madejski ◽  
Krzysztof Fuzowski
Keyword(s):  
Machine Learning ◽  
District Heating ◽  
Ambient Air ◽  
Measuring System ◽  
Machine Learning Method ◽  
Learning Method ◽  
Demand Model ◽  
District Heating System ◽  
Heat Demand ◽  
Extreme Gradient Boosting

Forecasting an hourly heat demand during different periods of district heating network operation is essential to optimize heat production in the CHP plant. The paper presents the heat demand model in the real district heating system with a peak load of 200 MW. The predictive model was developed with the use of the machine learning method based on the historical data. The XGBoost (Extreme Gradient Boosting) algorithm was applied to find the relation between actual heat demand and predictors such as weather data and behavioral parameters like an hour of the day, day of week, and month. The method of model training and evaluating was discussed. The results were assessed by comparing hourly heat demand forecasts with actual values from a measuring system located in the CHP plant. The RMSE and MAPE error for the analysed time period were calculated and then benchmarked with an exponential regression model supplied with ambient air temperature. It was found that the machine learning method allows to obtain more accurate results due to the incorporation of additional predictors. The MAPE and RMSE for the XGBoost model in the day-ahead horizon were 6.9% and 8.7MW, respectively.

Solar Renewable Energy Credit Price Volatility and Investment Returns: Have Policies Stranded Capital Investment?

Commonwealth ◽  
2017 ◽  
Vol 19 (1) ◽  
Author(s):  
Somayeh Youssefi ◽  
Patrick L. Gurian
Keyword(s):  
Solar Energy ◽  
Capital Investment ◽  
Supply And Demand ◽  
Price Volatility ◽  
The United States ◽  
Investment Model ◽  
Capital Investments ◽  
Demand Model ◽  
Market Prices ◽  
Investment Returns

Pennsylvania is one of a number of U.S. states that provide incentives for the generation of electricity by solar energy through Solar Renewal Energy Credits (SRECs). This article develops a return on investment model for solar energy generation in the PJM (mid-­Atlantic) region of the United States. Model results indicate that SREC values of roughly $150 are needed for residential scale systems to break even over a 25-­year project period at 3% interest. Market prices for SRECs in Pennsylvania have been well below this range from late 2011 through the first half of 2016, indicating that previous capital investments in solar generation have been stranded as a result of steep declines in the value of SRECs. A simple conceptual supply and demand model is developed to explain the sharp decline in market prices for SRECs. Also discussed is a possible policy remedy that would add unsold SRECs in a given year to the SREC quota for the subsequent year.

The impact of regulations on labor markets in the U.S.

2019 ◽  
Vol 1 (1) ◽  
pp. 36-40
Author(s):  
Souad Adnane
Keyword(s):  
Supply And Demand ◽  
Demand Model ◽  
Lead Teachers ◽  
Associate's Degree ◽  
Childcare Workers ◽  
Educational Requirements ◽  
The Cost ◽  
Women's Labor Force Participation ◽  
The Impact ◽  
The U.S

The District of Columbia (DC) Office of the Superintendent of Education (OSSE) issued in December 2016 new educational requirements for childcare workers, according to which, all childcare center directors in the District must earn a bachelor’s degree by December 2022 and all lead teachers an associate’s degree by December 2020 (Institute for Justice, 2018). Moreover, DC has one of the lowest staff-child ratios in the country. How are regulations pertaining to childcare workers’ qualifications and staff-child ratio affecting the childcare market in DC? The present paper is an attempt to answer this question first by analyzing the effects of more stringent regulations on the cost and availability of childcare in the U.S based on existing studies. It also uses the basic supply and demand model to examine the possible impact of the new DC policy on the cost, quality and supply of childcare in the District and how it will affect working parents, especially mothers. Next, the paper discusses the impact of deregulation based on simulations and regressions conducted by studies covering the U.S., and implications for quality. It concludes that more stringent childcare regulations, regarding educational requirements and staff-child ratios, are associated with a reduced number of childcare centers and a higher cost, and eventually affects women’s labor force participation.

An Aggregate-Demand Model for Automobiles

1965 ◽  
Vol 38 (3) ◽  
pp. 252 ◽  
Author(s):  
Thomas R. Dyckman
Keyword(s):  
Aggregate Demand ◽  
Demand Model
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