Results of monitoring of AMR systems in Norway: analysis of metered data and definition of the performance parameters

Morch, Andrei; Graabak, Ingeborg; Feilberg, Nicolai

doi:10.1049/cp.2009.0769

Cited by 3 publications

(3 citation statements)

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“…We have used data from two different sources: SINTEF Energy Research [3] and transmission level data from British Columbia (BC Hydro). The SINTEF data was recorded from 2004 through 2006, the BC Hydro dataset covers the period 2004 through 2010.…”

Section: Methodsmentioning

confidence: 99%

Effects of scale on load prediction algorithms

Tidemann

Høverstad

Langseth

et al. 2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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Section: Methodsmentioning

confidence: 99%

Effects of scale on load prediction algorithms

Tidemann

Høverstad

Langseth

et al. 2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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“…Our data comes from two sources: a dataset from an earlier research project from SINTEF Energy Research in cooperation with several Norwegian utility companies [11]. The original dataset contains load measurements from a small number of Norwegian cities and municipalities, in the period 2004-2006.…”

Section: A Datasetmentioning

confidence: 99%

Effects of data cleansing on load prediction algorithms

Høverstad

Tidemann

Langseth

2013

2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG)

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Abstract-The rollout of advanced metering infrastructure that is planned in many countries worldwide will lead to a massive inflow of data from moderately reliable sensory equipment. In principle, this will make intelligent and automated planning and operation possible at an increasingly finer scale in the electric grid. However, errors can creep into the meter data, either from faulty sensors or during transmission from the meters to the database. This work studies the role of data cleansing as a preprocessing step for short-term (24-hour) power load prediction. We focus on cleansing and prediction at several levels of granularity, from the transmission level via distribution substations down to single households.We believe that preprocessing filters such as cleansing should lead to more robustness and/or precision in the subsequent processing step. However, load cleansing frameworks tend to make the popular assumption of normally and independently distributed noise in the time series. We show that this is incorrect at the diurnal level, due to the characteristic pattern of power consumption, with two peak loads during daytime and a nighttime trough. Moreover, we present empirical evidence that a preprocessing step based on this assumption fails to contribute positively to the performance of the subsequent prediction step. To rectify this problem, we suggest to subtract the average power load consumption in a given period before cleansing. We present empirical evidence that this improves the robustness and efficiency of load cleansing as a preprocessing step. Data cleansing and load prediction is performed by a system that searches out parameters using an evolutionary approach.

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“…All these outcomes may help energy providers, distributors and reseller companies make better investment decisions and strategic planning. Despite all the advantages over the "manual reading" procedure, the data from AMR had been occasionally reported as "missing" and one of the possible causes is due to communication faults [12] [13]. All communication devices that are wirelessly connected are vulnerable to risks such as lost connectivity, call drop-outs and out-of-coverage signaling, especially when collecting real-time data in a short period of interval [14].…”

Section: Introductionmentioning

confidence: 99%

Measurements of Mobile Signal Received Power Level at Ringlet, Cameron Highland, Malaysia

Suliman

Hashim²,

Ismail

et al. 2018

IJET

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Tenaga Nasional, the electric utility company in Peninsular Malaysia have been installing electronic meters that can establish wireless connectivity over the mobile phone network since 2005. Several locations including Ringlet, Cameron Highland have been pointed out as having difficulties in establishing a stable connection to the national main server. Empirical investigations had been carried out to determine the availability of service coverage and the associated signal received power levels at the said location. The findings are reported in this article. The outcomes from the measurement campaign were used to deduce the likely solution that is capable of establishing a more reliable link. Field trials involving installations of high-gain antennas and signal boosters as potential solutions were also carried out at selected sites within the district. It is the objective of the study to develop a total solution that can ultimately address the communication issue.

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Results of monitoring of AMR systems in Norway: analysis of metered data and definition of the performance parameters

Cited by 3 publications

References 0 publications

Effects of scale on load prediction algorithms

Effects of scale on load prediction algorithms

Effects of data cleansing on load prediction algorithms

Measurements of Mobile Signal Received Power Level at Ringlet, Cameron Highland, Malaysia

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