scholarly journals Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ngoc-Son Truong ◽  
Ngoc-Tri Ngo ◽  
Anh-Duc Pham
Keyword(s):  
Energy Efficiency ◽  
Energy Use ◽  
Predictive Accuracy ◽  
Residential Buildings ◽  
Absolute Error ◽  
Photovoltaic System ◽  
Percentage Error ◽  
Support Vector ◽  
Building Energy Efficiency ◽  
Improve Energy Efficiency

Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.

Energy Audits in the Service Sector Buildings

2014 ◽  
Author(s):  
Antonella Zanfardino ◽  
Luca Andreassi ◽  
Fabrizio Martini ◽  
Stefano Ubertini
Keyword(s):  
Energy Efficiency ◽  
Energy Saving ◽  
Service Sector ◽  
Energy Use ◽  
Hot Water ◽  
Building Energy Efficiency ◽  
Great Opportunity ◽  
Improve Energy Efficiency ◽  
Technical Systems ◽  
Energy Audits

In the last decade, the service sector had a very rapid growth, due to the so-called “tertiarisation” of the economy. Accordingly, the energy consumption, mainly attributable to public and private buildings, is rapidly growing, thus making buildings energy saving one of the main issues of the energy policy at regional, national and international levels. To this aim, we developed an effective methodology to improve energy efficiency of the service sector buildings. This may represent a handy great opportunity to save natural and economic resources, especially where the buildings structure and the technical systems are old, the maintenance activities are not carefully carried out or a systematic energy management is not applied. Nevertheless, actions in this direction are often considered too expensive and complicated, if compared with residential energy optimization, because of the big extension, the variety of activities and the high number of occupants typical of the service sector buildings. The developed approach for energy audits aims to investigate the energy aspects of existing non-domestic buildings in a structured way, in order to clearly identify their energy saving potential and to improve their energy performances. The main goal of the study is defining a general methodology to analyze the current energy use and consumption considering a limited number of their peculiar elements such as dimensions, activities, users behavior, technical systems data and energy bills. Furthermore, these informations are completed by an appropriate energy measuring campaign. All the possible energy uses in service buildings are taken into account (i.e. lighting, ventilation, air conditioning, hot water production). The results obtained from the analysis allow to evaluate a global level of building energy efficiency, and to identify those single areas, specific systems or everyday activities where energy is wasted. These considerations also provide basis for programming cost-effective energy saving action plans. The effectiveness of the proposed methodology is demonstrated through a case study for an Administrative Center building in Rome, Italy. Results demonstrate the methodology reliability and the cost reduction potentialities.

scholarly journals ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS, LESS STRAIGHT FORWARD THEN PRESUMED

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Hugo Hens
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Energy Use ◽  
Low Voltage ◽  
Residential Buildings ◽  
Primary Energy ◽  
Heating And Cooling ◽  
Net Zero ◽  
Energy Conserving ◽  
New Construction

Since the 1990s, the successive EU directives and related national or regional legislations require new construction and retrofits to be as much as possible energy-efficient. Several measures that should stepwise minimize the primary energy use for heating and cooling have become mandated as requirement. However, in reality, related predicted savings are not seen in practice. Two effects are responsible for that. The first one refers to dweller habits, which are more energy-conserving than the calculation tools presume. In fact, while in non-energy-efficient ones, habits on average result in up to a 50% lower end energy use for heating than predicted. That percentage drops to zero or it even turns negative in extremely energy-efficient residences. The second effect refers to problems with low-voltage distribution grids not designed to transport the peaks in electricity whensunny in summer. Through that, a part of converters has to be uncoupled now and then, which means less renewable electricity. This is illustrated by examples that in theory should be net-zero buildings due to the measures applied and the presence of enough photovoltaic cells (PV) on each roof. We can conclude that mandating extreme energy efficiency far beyond the present total optimum value for residential buildings looks questionable as a policy. However, despite that, governments and administrations still seem to require even more extreme measurements regarding energy efficiency.

scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

scholarly journals Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1309 ◽  
Author(s):  
Tomasz Szul ◽  
Stanisław Kokoszka
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Set Theory ◽  
Rough Set ◽  
Energy Demand ◽  
Rough Set Theory ◽  
Residential Buildings ◽  
Simple Procedure ◽  
Energy Performance ◽  
Improve Energy Efficiency

In many regions, the heat used for space heating is a basic item in the energy balance of a building and significantly affects its operating costs. The accuracy of the assessment of heat consumption in an existing building and the determination of the main components of heat loss depends to a large extent on whether the energy efficiency improvement targets set in the thermal upgrading project are achieved. A frequent problem in the case of energy calculations is the lack of complete architectural and construction documentation of the analyzed objects. Therefore, there is a need to search for methods that will be suitable for a quick technical analysis of measures taken to improve energy efficiency in existing buildings. These methods should have satisfactory results in predicting energy consumption where the input is limited, inaccurate, or uncertain. Therefore, the aim of this work was to test the usefulness of a model based on Rough Set Theory (RST) for estimating the thermal energy consumption of buildings undergoing an energy renovation. The research was carried out on a group of 109 thermally improved residential buildings, for which energy performance was based on actual energy consumption before and after thermal modernization. Specific sets of important variables characterizing the examined buildings were distinguished. The groups of variables were used to estimate energy consumption in such a way as to obtain a compromise between the effort of obtaining them and the quality of the forecast. This has allowed the construction of a prediction model that allows the use of a fast, relatively simple procedure to estimate the final energy demand rate for heating buildings.

scholarly journals Spatial and Behavioral Thermal Adaptation in Net Zero Energy Buildings: An Exploratory Investigation

2020 ◽  
Vol 12 (19) ◽  
pp. 7961 ◽  
Author(s):  
Shady Attia
Keyword(s):  
Energy Efficiency ◽  
Thermal Comfort ◽  
Energy Use ◽  
Thermal Adaptation ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Zero Energy ◽  
Net Zero Energy ◽  
Net Zero ◽  
Energy Efficiency Standards

Climate responsive design can amplify the positive environmental effects necessary for human habitation and constructively engage and reduce the energy use of existing buildings. This paper aims to assess the role of the thermal adaptation design strategy on thermal comfort perception, occupant behavior, and building energy use in twelve high-performance Belgian households. Thermal adaptation involves thermal zoning and behavioral adaptation to achieve thermal comfort and reduce energy use in homes. Based on quantitative and qualitative fieldwork and in-depth interviews conducted in Brussels, the paper provides insights on the impact of using mechanical systems in twelve newly renovated nearly- and net-zero energy households. The article calls for embracing thermal adaptation as a crucial design principle in future energy efficiency standards and codes. Results confirm the rebound effect in nearly zero energy buildings and the limitation of the current building energy efficiency standards. The paper offers a fresh perspective to the field of building energy efficiency that will appeal to researchers and architects, as well as policymakers.

scholarly journals What Affects the Progress and Transformation of New Residential Building Energy Efficiency Promotion in China: Stakeholders’ Perceptions

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1027 ◽  
Author(s):  
Yinan Li ◽  
Neng Zhu ◽  
Beibei Qin
Keyword(s):  
Energy Efficiency ◽  
Policy Design ◽  
Residential Buildings ◽  
Policy Instruments ◽  
Residential Building ◽  
Policy Implications ◽  
Building Energy Efficiency ◽  
Governance Arrangement ◽  
Market Needs ◽  
Research Assumptions

Nationwide energy efficiency (EE) promotion of new residential buildings is affected by multiple factors regarding policies, markets, technologies, capacities, and economics. The perceived influences of these factors by stakeholders are crucial to the effectiveness evaluation of current policies and the selection of policy instruments. However, they are normally assumed or taken for granted. The knowledge gap between stakeholders’ perceptions and research assumptions may lead to researchers’ recognition bias. Correspondingly, this paper aims to identify the significant factors, perceived by frontline stakeholders, influencing nationwide EE promotion of new residential buildings before 2020 and 2030. Factors were collected through literature review and their influence were evaluated via Analytical Hierarchy Process based on the data collected in the questionnaires distributed to 32 institutes. The theory of Nested Policy Design Framework and Policy Environment was used to structure the hierarchy and generate policy implications. Results indicate that (1) policy factors are of the greatest influence before 2020 and market perfection factors will have great influences from 2020 to 2030, indicating the transformation of governance arrangement to “market-based” and “network-based” from the current legal-based system; and (2) factors regarding market needs are of significant influence in both terms, revealing the way the transformation should be accomplished.

scholarly journals The New Market Transformation Needed for Commercial Building Energy Efficiency

2016 ◽  
Vol 9 (1) ◽  
pp. 229
Author(s):  
Valerie Patrick ◽  
Leslie A. Billhymer ◽  
William Shephard
Keyword(s):  
Energy Efficiency ◽  
Energy Use ◽  
Building Energy ◽  
Department Of Energy ◽  
Building Energy Efficiency ◽  
Commercial Building ◽  
Innovation Cluster ◽  
Stakeholder Perspectives ◽  
Regional Market ◽  
Market Transformation

The U.S. Department of Energy [DOE] established the Consortium for Building Energy Innovation [CBEI] to address commercial building energy efficiency as an innovation cluster, where the regional market context (Note 1) guides the research agenda for market transformation (Porter, 2001). CBEI develops content to support Advanced Energy Retrofits (AERs), a retrofit which results in 50% or greater reduction in building energy use, in small- and medium- sized commercial buildings (less than 250 000 ft<sup>2</sup>). The challenge is collecting input for a market with many stakeholders so that a strategy emerges to implement AERs. This research applies systems and complexity theories to develop a strategy to promote the emergence of AERs in this market incorporating multiple stakeholder perspectives (Note 2).

Method Evaluation for Short-Term Wind Speed Prediction Considering Multi Regions in Japan

2016 ◽  
Vol 28 (5) ◽  
pp. 681-686
Author(s):  
Ikki Tanaka ◽  
◽  
Hiromitsu Ohmori
Keyword(s):  
Wind Speed ◽  
Energy Use ◽  
Absolute Error ◽  
External Input ◽  
Support Vector ◽  
Prediction Errors ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
One Step ◽  
Text Prediction

[abstFig src='/00280005/09.jpg' width='300' text='Prediction errors at observation points' ] Wind energy use is being developed worldwide. Improving wind speed forecasting techniques has become important due to their economic impact on power system operation with increasing wind power penetration. Wind speed prediction is generally difficult due to wind’s intermittent nature, so many approaches have been proposed by researchers. The viability of these techniques has been verified, however, in only a certain few areas, rather than being evaluated quantitatively in many different locations. We use data from different parts of Japan for one-step-ahead prediction and applied different approaches at each point, which was then evaluated such as mean absolute error. We used the persistent model, the ARMA-GARCH model, the nonlinear autoregressive network with external input (NARX), the recurrent neural network (RNN), and support vector regression (SVR). Our results suggest that it is difficult to create the same model which minimizes error in all areas, confirming the need for individual predictors for individual regions.

Sign in / Sign up

Export Citation Format

Share Document