Smart meters and household electricity consumption: A case study in Ireland

2017 ◽  
Vol 29 (1) ◽  
pp. 131-146 ◽  
Author(s):  
Gordon Rausser ◽  
Wadim Strielkowski ◽  
Dalia Štreimikienė
Keyword(s):  
Electricity Consumption ◽  
Smart Meters

scholarly journals Towards DC Energy Efficient Homes

2021 ◽  
Vol 11 (13) ◽  
pp. 6005
Author(s):  
Daniel Villanueva ◽  
Moisés Cordeiro-Costas ◽  
Andrés E. Feijóo-Lorenzo ◽  
Antonio Fernández-Otero ◽  
Edelmiro Miguez-García
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Energy Efficient ◽  
Electricity Consumption ◽  
Real Data ◽  
Mean Values ◽  
Led Lighting ◽  
Shed Light

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values.

AI and privacy concerns: a smart meter case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jillian Carmody ◽  
Samir Shringarpure ◽  
Gerhard Van de Venter
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Consumer Information ◽  
Data Sets ◽  
Smart Meter ◽  
Smart Meters ◽  
Content Type ◽  
Privacy Concerns ◽  
Privacy Legislation

Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

Analiza potencijala za uvođenje sistema pametnih mreža u Bosni i Hercegovini

2021 ◽  
Vol 23 (3) ◽  
pp. 61-65
Author(s):  
Jasmina Imamović ◽  
Sanda Midžić Kurtagić ◽  
Esma Manić ◽  
Keyword(s):  
Renewable Energy ◽  
Smart Grid ◽  
Technological Development ◽  
Energy Resource ◽  
Electricity Consumption ◽  
Policy Framework ◽  
Distributed Energy ◽  
Smart Meters ◽  
Distributed Energy Resource ◽  
Charging Stations

The paper presents an analysis of the current situation regarding the development of an electricity distribution network and potential for a smart grid development in the selected pilot region of Bosnia and Herzegovina. Apart from the policy framework assessment, several indicator based criteria were included in the scope of analysis: share of renewable energy and renewable energy as distributed energy resource, total share of distributed energy resources, a number of installed smart meters for measuring electricity consumption, a number of charging stations for electric vehicles, energy storage capacities and technological development. The overall analysis of the assessment has been done by normalization of the calculated values of the indicators on a scale of 1-5. The indicators have showed that the smart grid sector in the Region is currently underdeveloped.

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Medhat Abd el Azem El Sayed Rostum ◽  
Hassan Mohamed Mahmoud Moustafa ◽  
Ibrahim El Sayed Ziedan ◽  
Amr Ahmed Zamel
Keyword(s):  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Electricity Consumption ◽  
Whale Optimization Algorithm ◽  
Computational Time ◽  
Smart Meters ◽  
Content Type ◽  
Individual Level ◽  
Consumption Values ◽  
Whale Optimization

Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

FEASIBILITY ANALYSIS OF A PHOTOVOLTAIC/BATTERY ENERGY STORAGE HYBRID SYSTEM: AN HOURLY ESTIMATION BASED APPROACH AND A REAL LIFE CASE STUDY

2017 ◽  
Vol 12 (3) ◽  
pp. 54-68 ◽  
Author(s):  
Fehmi Görkem Üçtuğ ◽  
Vedat Can Baltalı
Keyword(s):  
Energy Storage ◽  
Energy Demand ◽  
Real Life ◽  
Electricity Consumption ◽  
System Size ◽  
Battery Energy Storage ◽  
Heating And Cooling ◽  
Weather Changes ◽  
Battery Energy

This study has been undertaken to develop a consumer-oriented feasibility method for a hybrid photovoltaic (PV)-battery energy storage (BES) system by analyzing a real life house in Istanbul, Turkey, as a case study. The hourly electricity demand of the house was estimated by carrying out a detailed survey of the life style and daily habits of the household. No algorithm of any kind was used for the estimation of the energy demand with the exception of relating the lighting requirement to the daylight hours and the heating and cooling requirements to the seasonal weather changes. The developed method estimates the annual demand with an overall error of 8.68%. The net grid dependency and the feasibility of the PV-BES system was calculated for different combinations of PV and BES system sizes. It was found that when the maximum available roof area is used for PV installation and when the BES system size is increased, it is possible to achieve almost zero net grid dependency, and it is estimated that houses that are in regions with more abundant solar radiation and/or with lower annual electricity consumption, can reach zero net grid dependency. However, the feasibility indicator, which is the payback period, turned out to be no less than 25 years in any of the scenarios. The reasons for the infeasibility are the high prices of PV and BES systems as well as the current restriction in the regulations in Turkey, which prevents BES system owners from participating in unlicensed energy generation schemes and selling excess electricity back to the grid. In order to overcome this situation, regulations should be updated to allow BES system owners to benefit from feed-in-tariff schemes, thereby increasing the popularity of both PV and BES usage in Turkey.

scholarly journals Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 541 ◽  
Author(s):  
Sourav Khanna ◽  
Victor Becerra ◽  
Adib Allahham ◽  
Damian Giaouris ◽  
Jamie M. Foster ◽  
...  
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Energy Transport ◽  
Model Development ◽  
Software Tool ◽  
Load Flow ◽  
Smart Devices ◽  
Smart Meters ◽  
Isle Of Wight

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.

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