scholarly journals Enabling Demand Side Management: Heat Demand Forecasting at City Level

Materials ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 202 ◽  
Author(s):  
Petri Hietaharju ◽  
Mika Ruusunen ◽  
Kauko Leiviskä
Keyword(s):  
Energy Efficiency ◽  
Demand Forecasting ◽  
Demand Side Management ◽  
Measurement Information ◽  
Demand Side ◽  
Demand Forecast ◽  
Building Stock ◽  
Improve Energy Efficiency ◽  
Heat Demand ◽  
The City

Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area. District heating data at the city level from more than 4000 different buildings was utilized in the validation of the forecast models. Forecast simulations with the applied models and measured data showed that, during the heating season, the relative error of the city level heat demand forecast for 48 h was 4% on average. In individual buildings, the accuracy of the models varied based on the building type and heat demand pattern. The forecasting accuracy, the limited amount of measurement information and the short time required for model calibration enable the models to be applied to the whole building stock. This should enable demand side management and lead to the predictive optimization of heat demand at city level, leading to increased energy efficiency.

scholarly journals Demand Side Management in Pellet Production: Internal and External Factors

2014 ◽  
Vol 14 (1) ◽  
pp. 30-35 ◽  
Author(s):  
Haralds Vigants ◽  
Dagnija Blumberga ◽  
Ivars Veidenbergs
Keyword(s):  
Energy Efficiency ◽  
Energy Management ◽  
Energy Demand ◽  
Demand Side Management ◽  
External Factors ◽  
Technological Equipment ◽  
Demand Side ◽  
Improve Energy Efficiency ◽  
Pellet Production ◽  
Internal And External Factors

Abstract This paper demonstrates a demand side management case study: how to save energy and how research and data analysis help to create an energy management system in a pellet production facility; and shows ways to implement the EU energy efficiency directive in production facilities. The study carried out in this research serves as a far-reaching step that can be taken to improve energy efficiency during the operation mode of technological equipment. The benchmarking methodology is used for analysis of results. Internal and external factors and indicators, which affect energy management potential in pellet production are analysed. Analysis of external factors is based on the state legal framework regulating the development of the energy sector. Methodology on the analysis of energy demand includes the internal energy management of an enterprise. The experimental results discussed in this paper show that particular steps, which are oriented to specific use of technological equipment, could play significant role in energy efficiency improvement in industry which is illustrated by the pre-milling process in the pellet production system using power.

scholarly journals Assigning Energetic Archetypes to a Digital Cadastre and Estimating Building Heat Demand. An Example from Hamburg, Germany

2020 ◽  
Vol 24 (1) ◽  
pp. 233-253
Author(s):  
Ivan Dochev ◽  
Hannes Seller ◽  
Irene Peters
Keyword(s):  
Energy Efficiency ◽  
Model Performance ◽  
Building Stock ◽  
Heat Demand ◽  
Consumption Data ◽  
Construction Type ◽  
Building Heat ◽  
The City ◽  
Source Of Error ◽  
Different Levels

AbstractIn view of the relatively large energy consumption of national building stocks, many cities and municipalities start to prepare energetic building stock models to monitor energy efficiency and plan policies at city or regional scales. In many cases, data on individual buildings is not available. A usual approach to this is the “archetype” approach – classifying the building stock into energetic types (archetypes). This classification is usually based on non-energetic properties available in digital cadastres (construction type, year of construction etc.) and can be a large source of error. We present our research into the difficulties and pitfalls associated with such an approach using the city of Hamburg as an example. In the end, we compare the modelled estimates with consumption data at three different levels to evaluate model performance.

scholarly journals Predictive Optimization of the Heat Demand in Buildings at the City Level

2019 ◽  
Vol 9 (10) ◽  
pp. 1994
Author(s):  
Petri Hietaharju ◽  
Mika Ruusunen ◽  
Kauko Leiviskä ◽  
Marko Paavola
Keyword(s):  
Energy Efficiency ◽  
District Heating ◽  
Short Term ◽  
Indoor Temperature ◽  
Improve Energy Efficiency ◽  
Apartment Buildings ◽  
Heat Demand ◽  
Demand Models ◽  
The City ◽  
Modelling Approach

Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

Production Process Modeling for Demand Side Management

2017 ◽  
Vol 871 ◽  
pp. 77-86
Author(s):  
Stefanie Kabelitz ◽  
Sergii Kolomiichuk
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Supply And Demand ◽  
Demand Side Management ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Business Practices ◽  
Demand Side ◽  
Manufacturing Companies ◽  
Consumption Data

The supply of electricity is growing increasingly dependent on the weather as the share of renewable energies increases. Different measures can nevertheless maintain grid reliability and quality. These include the use of storage technologies, upgrades of the grid and options for responsiveness to supply and demand. This paper focuses on demand side management and the use of flexibility in production processes. First, the framework of Germany’s energy policy is presented and direct and indirect incentives for businesses to seek as well as to provide flexibility capabilities are highlighted. Converting this framework into a mixed integer program leads to multi-objective optimization. The challenge inherent to this method is realistically mapping the different objectives that affect business practices directly and indirectly in a variety of laws. An example is introduced to demonstrate the complexity of the model and examine the energy flexibility. Second, manufacturing companies’ energy efficiency is assessed under the frequently occurring conditions of heavily aggregated energy consumption data and of information with insufficient depth of detail to perform certain analyses, formulate actions or optimize processes. The findings obtained from the energy assessment and energy consumption projections are used to model the production system’s energy efficiency and thus facilitate optimization. Methods of data mining and machine learning are employed to project energy consumption. Aggregated energy consumption data and different production and environmental parameters are used to assess indirectly measured consumers and link projections of energy consumption with the production schedule.

Semantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities

2021 ◽  
pp. 515-539
Author(s):  
Álvaro Sicilia ◽  
Gonçal Costa ◽  
Leandro Madrazo
Keyword(s):  
Energy Efficiency ◽  
Holistic Approach ◽  
Energy Performance ◽  
Building Stock ◽  
Semantic Web Technologies ◽  
Improve Energy Efficiency ◽  
Semantic Data ◽  
Multiple Data ◽  
Efficiency Measures ◽  
Multiple Domains

The assessment of building energy performance requires data from multiple domains (energy, architecture, planning, economy) and scales (building, district, city) to be processed with a diversity of applications used by experts from various fields. In order to properly assess the performance of the building stock, and to develop and apply the most effective energy efficiency measures, it is necessary to adopt a comprehensive, holistic approach. In this chapter, three research projects are presented which apply Semantic Web technologies to create energy data models from multiple data sources and domains in order to support decision making in energy efficient building renovation projects: SEMANCO, OptEEmAL, and OPTIMUS. A final reflection on the results achieved in these projects and their links to ongoing research on digital twins is presented.

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