scholarly journals Optimizing the energy consumption and operational availability of point heaters using weather forecasts

2014 ◽  
Author(s):  
C. Weiß
Keyword(s):  
Energy Consumption ◽  
Weather Forecasts

Robust anticipative energy management system: Application of a smart building platform

2016 ◽  
Vol 38 (2) ◽  
pp. 226-248 ◽  
Author(s):  
Minh-Hoang Le ◽  
Stephane Ploix
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Optimization Problem ◽  
Energy Allocation ◽  
Electricity Production ◽  
Variable Cost ◽  
Parametric Uncertainties ◽  
Management Problem ◽  
Weather Forecasts ◽  
Home Energy Management

Robust energy management in buildings is addressed in this paper. The energetic impact of buildings in the current energetic context is first presented. Then the studied optimization problem is defined as the optimal management of production and consumption activities in buildings. A scheduling problem is identified to adjust the energy consumption to both the energy cost and the user’s comfort. The available flexibility of the services provided by domestic appliances is used to compute optimal energy plans. These flexibilities are associated to time windows or heating storage abilities. A constraints formulation of the energy allocation problem is given. A derived mixed linear program is used to solve this problem. The energy consumption in houses is very dependent on uncertain data such as weather forecasts and inhabitants’ activities. Parametric uncertainties are introduced in the home energy management problem in order to provide robust energy allocation. Robust linear programming is implemented. A scenario-based approach is implemented to face this robust optimization problem. Practical application: Because of the increasing part of renewable energy in electricity production, which is difficult to control, consumers will have to become more involved in the grid management. This paper states the problem of energy management in buildings and describes the optimization problem defined to adjust the energy consumption of buildings to production constraints. This decision system is based on the weather forecasts and a variable cost of electricity. Parametric uncertainties on the data are taken into account in order to propose robust energy planning in which a performance is guaranteed over the expected data.

scholarly journals Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

2019 ◽  
Vol 11 (12) ◽  
pp. 3328 ◽  
Author(s):  
Konrad Bogner ◽  
Florian Pappenberger ◽  
Massimiliano Zappa
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Renewable Energy Sources ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Uncertainty Estimates ◽  
Ensemble Weather Forecasts

Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects

Lay Perceptions of Energy Consumption

2009 ◽  
Author(s):  
Shahzeen Z. Attari ◽  
Michael L. DeKay ◽  
Cliff I. Davidson ◽  
Wandi Bruine de Bruin
Keyword(s):  
Energy Consumption ◽  
Lay Perceptions

Study on Energy Consumption by Land Cargo Transportation

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Shunquan Huang ◽  
Siqin Yu ◽  
Zhongmin Liu
Keyword(s):  
Energy Consumption ◽  
Cargo Transportation

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

Influence of trees on the energy consumption of a social housing in mid-western Brazil

2019 ◽  
pp. 53-65
Author(s):  
Renata Domingos ◽  
Emeli Guarda ◽  
Elaise Gabriel ◽  
João Sanches
Keyword(s):  
Energy Consumption ◽  
Solar Radiation ◽  
Thermal Comfort ◽  
Air Conditioning ◽  
Computational Simulation ◽  
Building Energy ◽  
General Idea ◽  
Heat Island ◽  
Study Region ◽  
Ample Evidence

In the last decades, many studies have shown ample evidence that the existence of trees and vegetation around buildings can contribute to reduce the demand for energy by cooling and heating. The use of green areas in the urban environment as an effective strategy in reducing the cooling load of buildings has attracted much attention, though there is a lack of quantitative actions to apply the general idea to a specific building or location. Due to the large-scale construction of high buildings, large amounts of solar radiation are reflected and stored in the canyons of the streets. This causes higher air temperature and surface temperature in city areas compared to the rural environment and, consequently, deteriorates the urban heat island effect. The constant high temperatures lead to more air conditioning demand time, which results in a significant increase in building energy consumption. In general, the shade of the trees reduces the building energy demand for air conditioning, reducing solar radiation on the walls and roofs. The increase of urban green spaces has been extensively accepted as effective in mitigating the effects of heat island and reducing energy use in buildings. However, by influencing temperatures, especially extreme, it is likely that trees also affect human health, an important economic variable of interest. Since human behavior has a major influence on maintaining environmental quality, today's urban problems such as air and water pollution, floods, excessive noise, cause serious damage to the physical and mental health of the population. By minimizing these problems, vegetation (especially trees) is generally known to provide a range of ecosystem services such as rainwater reduction, air pollution mitigation, noise reduction, etc. This study focuses on the functions of temperature regulation, improvement of external thermal comfort and cooling energy reduction, so it aims to evaluate the influence of trees on the energy consumption of a house in the mid-western Brazil, located at latitude 15 ° S, in the center of South America. The methodology adopted was computer simulation, analyzing two scenarios that deal with issues such as the influence of vegetation and tree shade on the energy consumption of a building. In this way, the methodological procedures were divided into three stages: climatic contextualization of the study region; definition of a basic dwelling, of the thermophysical properties; computational simulation for quantification of energy consumption for the four facade orientations. The results show that the façades orientated to north, east and south, without the insertion of arboreal shading, obtained higher values of annual energy consumption. With the adoption of shading, the facades obtained a consumption reduction of around 7,4%. It is concluded that shading vegetation can bring significant climatic contribution to the interior of built environments and, consequently, reduction in energy consumption, promoting improvements in the thermal comfort conditions of users.

Sign in / Sign up

Export Citation Format

Share Document