scholarly journals Multilayer modeling of adoption dynamics in energy demand management

2020 ◽  
Vol 30 (1) ◽  
pp. 013153 ◽  
Author(s):  
Iacopo Iacopini ◽  
Benjamin Schäfer ◽  
Elsa Arcaute ◽  
Christian Beck ◽  
Vito Latora
Keyword(s):  
Energy Demand ◽  
Demand Management ◽  
Adoption Dynamics

scholarly journals Proposal and Implementation of a User-Centered Design Approach for Smart Grid Technologies: Example of the VERTPOM® Project

2021 ◽  
Vol 14 (4) ◽  
pp. 57
Author(s):  
Helios Raharison ◽  
Emilie Loup-Escande
Keyword(s):  
Distribution System ◽  
Smart Grids ◽  
Energy Demand ◽  
Demand Management ◽  
Real Life ◽  
Positive Energy ◽  
User Centered Design ◽  
Electrical Networks ◽  
Evaluation Approach ◽  
User Centered

Acting to preserve our planet as much as possible is no longer optional in today's world. To do so, Smart Grids within the framework of electrical networks - involving not only Distribution System Operators (DSOs), but also consumers in their Energy Demand Management (EDM) activity - represent an innovative and sustainable solution. However, the integration of Smart Grids into network management or into consumers' homes implies changes at several levels: organizational, social, psychological, etc. This is why it is essential to consider the human factor in the design of the technologies used in these Smart Grids. This paper proposes the integration of DSO operators and consumers within a user-centered evaluation approach in order to design Smart Grids that are sufficiently acceptable to users to enable Positive Energy Territories that produce more energy than they consume. This demonstration will be illustrated by the VERTPOM® project aiming at facilitating the use of renewable energies specific to each territory in order to contribute to the reduction of greenhouse gases and make the territories less dependent on traditional energies, and thus make Picardy (in France) a Positive Energy Territory. This paper presents the user-centered evaluation approach applied to three technologies (i.e., the VERTPOM-BANK® supervision tool intended for DSO operators, the private web portal and the IBox smart meter intended for households) from the upstream design phase to the implementation of the technologies in real-life situations.

scholarly journals Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4154 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Learning Strategy ◽  
Demand Management ◽  
Management Strategies ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

scholarly journals Energy Demand Management and Social Norms

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3779
Author(s):  
Bernadeta Gołębiowska ◽  
Anna Bartczak ◽  
Mikołaj Czajkowski
Keyword(s):  
Social Norms ◽  
Financial Incentives ◽  
Energy Demand ◽  
Consumer Preferences ◽  
Demand Management ◽  
Electricity Consumption ◽  
External Control ◽  
Revealed Preferences ◽  
Personal Norms ◽  
The Impact

The main objective of our study was investigating the impact of norms and financial motivation on the disutility of energy management for Polish households. We analyzed consumer preferences and willingness to accept demand-side management (DSM) programs. Choice experiment was applied for electricity contracts including external control of electricity consumption. Ajzen’s theory of planned behavior provided the theoretical framework of the study, which tested hypotheses about the impact of social norms on consumer choices of electricity contracts. We show that people with higher descriptive social norms about electricity consumption are less sensitive to the level of compensation and more responsive to the number of blackouts. People willing to sign a contract for financial reasons were less sensitive to the external control of electricity consumption and less inclined toward the status quo option. Injunctive social norms and personal norms had a non-significant impact on consumer decisions. We conclude that financial incentives can reduce the effect of the norms. Social and personal norms seem to be more important when we analyze the revealed preferences. European countries face significant challenges related to changes in energy policy. This study contributes to understanding the decisions of households and provides insights into the implementation of DSM.

In Search of Causal Relationship between FDI, GDP, and Energy Consumption - Evidence from China

2012 ◽  
Vol 524-527 ◽  
pp. 3388-3391 ◽  
Author(s):  
Kuo Cheng Kuo ◽  
Chi Ya Chang ◽  
Mei Hui Chen ◽  
Wei Yu Chen
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Environmental Protection ◽  
Granger Causality ◽  
Energy Demand ◽  
Negative Impact ◽  
Demand Management ◽  
Effective Control ◽  
Chinese Government ◽  
Negative Effects

The balance between economic growth and environmental protection has been the core concern of policy makers in developing countries for the past two decades. This study is one of the few studies to empirically inspect the relationship between economic growth, FDI, and energy consumption over the period 1978-2010 in China. The results reveal that there is a unidirectional Granger causality running from GDP to energy consumption. This suggests that increase of GDP will consume more energy and implementing of the energy conservation policies and energy demand management policies in China may not have negative impact on economic growth. Besides, a bi-directional Granger causality has been found between energy consumption and FDI. This implies that Chinese government should cautiously evaluate the positive and negative effects of FDI inflows and put efforts into making more effective control policies on environmental protection.

Nudging Citizen Scientists: Motivation, Behavior and Awareness in Energy Demand Management

2018 ◽  
Vol 2018 (1) ◽  
pp. 13379
Author(s):  
Francesco Cappa ◽  
Federica Rosso ◽  
Luca Giustiniano ◽  
Gianluca Squarcia
Keyword(s):  
Energy Demand ◽  
Demand Management
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