Smart meters for improved energy demand management

2015 ◽  
pp. 339-361 ◽  
Author(s):  
R. Bago ◽  
M. Campos
Keyword(s):  
Energy Demand ◽  
Demand Management ◽  
Smart Meters

scholarly journals Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings

2021 ◽  
Vol 11 (2) ◽  
pp. 500
Author(s):  
Fabrizio Pilo ◽  
Giuditta Pisano ◽  
Simona Ruggeri ◽  
Matteo Troncia
Keyword(s):  
Energy Demand ◽  
Data Analytics ◽  
Distribution Systems ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
A Priori ◽  
Energy Transition ◽  
Smart Meters ◽  
Large Databases ◽  
A New Technique

The energy transition for decarbonization requires consumers’ and producers’ active participation to give the power system the necessary flexibility to manage intermittency and non-programmability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non-existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO).

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 Using smart meters to identify social and technological impacts on residential water consumption

2011 ◽  
Vol 11 (5) ◽  
pp. 527-533 ◽  
Author(s):  
Cara Beal ◽  
Rodney A. Stewart ◽  
Anneliese Spinks ◽  
Kelly Fielding
Keyword(s):  
Water Use ◽  
Community Education ◽  
Demand Management ◽  
High Water ◽  
Management Policy ◽  
Smart Meters ◽  
Water Users ◽  
Residential Water Consumption ◽  
End Use ◽  
Demographic Surveys

Studies have shown that householders' perceptions of their water use are often not well matched with their actual water use. There has been less research, however, investigating whether this bias is related to specific types of end use and/or specific types of socio-demographic and socio-demographic household profiles. A high resolution smart metering study producing a detailed end use event registry as well as psycho-social and socio-demographic surveys, stock inventory audits and self-reported water diaries was completed for 250 households located in South-east Queensland, Australia. The study examined the contributions of end uses to total water use for each group identified as ‘low’, ‘medium’ or ‘high’ water users. Analyses were conducted to examine the socio-demographic variables such as income, percentage of water efficient stock, family size and composition, that characterise each self-identified water usage group. The paper concludes with a discussion of the general characteristics of groups that overestimate and underestimate their water use and how this knowledge can be used to inform demand management policy such as targeted community education programmes.

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.

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