Implementing Business Intelligence in Electricity Markets

2010 ◽  
pp. 283-295
Author(s):  
José Ramón Cancelo ◽  
Antoni Espasa
Keyword(s):  
Electricity Markets ◽  
Business Intelligence ◽  
Explicit Knowledge ◽  
Time Series Data ◽  
Contextual Information ◽  
Electricity Consumption ◽  
Point Of View ◽  
Series Data ◽  
Basic Ideas ◽  
Set Up

The authors elaborate on three basic ideas that should guide the implementation of business intelligence tools. First, the authors advocate for closing the gap between structured information and contextual information. Second, they emphasize the need for adopting the point of view of the organization to assess the relevance of any proposal. In the third place, they remark that any new tool is expected to become a relevant instrument to enhance the learning of the organization and to generate explicit knowledge. To illustrate their point, they discuss how to set up a forecasting support system to predict electricity consumption that converts raw time series data into market intelligence, to meet the needs of a major organization operating at the Spanish electricity markets.

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

scholarly journals Time series data measurement on electricity consumption for selected domestic appliances in typical terrace house of Malaysia

2019 ◽  
Vol 8 (3) ◽  
pp. 1144-1153
Author(s):  
Naja Aqilah ◽  
Sheikh Ahmad Zaki Shaikh Salim ◽  
Aya Hagishima ◽  
Nelidya Md Yusoff ◽  
Fitri Yakub
Keyword(s):  
Time Series Data ◽  
Electricity Consumption ◽  
Hot Water ◽  
Series Data ◽  
Small Contribution ◽  
Washing Machine ◽  
Air Conditioners ◽  
Power Characteristics ◽  
Data Measurement ◽  
Domestic Appliances

This paper describes the pattern of electricity consumption from total and selected domestic appliances at a typical terrace house in Malaysia. The measured appliances can be classified into four groups on the basis of pattern of use which are ‘standby’ (TV), ‘active’ (massage chair, charger of hand phone, laptop and power bank, washing machine, air-conditioners, iron, standing fan, shower heaters, rice cooker, toaster, microwave), ‘cold’ (refrigerator) and ‘cold and hot’ (water dispenser). The major contribution of monthly electricity consumption comes from ‘cold’ appliances that consume 118.8 kWh/month followed by ‘active’ appliances that consume 87.8 kWh/month and ‘cold and hot’ appliance with 52.5 kWh/month. ‘Standby’ appliances shown a small contribution to the total electricity with 0.9 kWh/month. The amount of energy consumed depends on time-of-use, power characteristics of particular appliances as well as occupancy period.

scholarly journals A Review of Post Privatization Performance of Ashaka Cement Manufacturing Company Nigeria PLC through the use of Financial Ratios

2012 ◽  
Vol 3 (12) ◽  
pp. 382-388
Author(s):  
Abubakar Muhammed Magaji
Keyword(s):  
Capital Investment ◽  
Time Series Data ◽  
Technical Committee ◽  
Series Data ◽  
Company Performance ◽  
Public Enterprises ◽  
Formal Sector ◽  
Set Up ◽  
Cement Manufacturing ◽  
Fixed Capital Investment

Privatization as a reform policy package has been adopted by both developed and developing countries’ economies. Nigeria as a developing country has large public enterprises which has about 57 percent of fixed capital investment and about 66 percent of formal sector employment by 1997. These enterprises performed below expectation due to multiple problems. Technical Committee on Privatization and Commercialization (TCPC) was set up to privatize the enterprises and the privatization have since commenced. The paper reviewed Ashaka cement company performance as a privatized enterprise after privatization. Managers of business organization must have reliable analytical tools for taking a rational decision. Ratio is one of such tools. Time series data from Ashaka Cement Company was used. The performance of the company has improved after privatization.

scholarly journals SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery

2021 ◽  
Vol 11 (22) ◽  
pp. 10873
Author(s):  
Silvestro R. Poccia ◽  
K. Selçuk Candan ◽  
Maria Luisa Sapino
Keyword(s):  
Time Series ◽  
Motif Discovery ◽  
Time Series Data ◽  
Contextual Information ◽  
Multimedia Data ◽  
Series Data ◽  
Motif Search ◽  
Fixed Subset ◽  
Simplifying Assumptions ◽  
Expensive Process

A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals for multi-variate motif discovery. Unfortunately, motif search among multiple variates is an expensive process, as the potential number of sub-spaces in which a pattern can occur increases exponentially with the number of variates. Consequently, many multi-variate motif search algorithms make simplifying assumptions, such as searching for motifs across all variates individually, assuming that the motifs are of the same length, or that they occur on a fixed subset of variates. In this paper, we are interested in addressing a relatively broad form of multi-variate motif detection, which seeks frequently occurring patterns (of possibly differing lengths) in sub-spaces of a multi-variate time series. In particular, we aim to leverage contextual information to help select contextually salient patterns and identify the most frequent patterns among all. Based on these goals, we first introduce the contextually salient multi-variate motif (CS-motif) discovery problem and then propose a salient multi-variate motif (SMM) algorithm that, unlike existing methods, is able to seek a broad range of patterns in multi-variate time series.

scholarly journals Smart Helmet: Thresh-Learner–Online Machine Learning on Data Streams

2019 ◽  
Vol 9 (1S3) ◽  
pp. 466-473
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Real Life ◽  
General Purpose ◽  
Streaming Data ◽  
Series Data ◽  
Dynamic Threshold ◽  
Wide Range ◽  
Set Up

Today, with an enormous generation and availability of time series data and streaming data, there is an increasing need for an automatic analyzing architecture to get fast interpretations and results. One of the significant potentiality of streaming analytics is to train and model each stream with unsupervised Machine Learning (ML) algorithms to detect anomalous behaviors, fuzzy patterns, and accidents in real-time. If executed reliably, each anomaly detection can be highly valuable for the application. In this paper, we propose a dynamic threshold setting system denoted as Thresh-Learner, mainly for the Internet of Things (IoT) applications that require anomaly detection. The proposed model enables a wide range of real-life applications where there is a necessity to set up a dynamic threshold over the streaming data to avoid anomalies, accidents or sending alerts to distant monitoring stations. We took the major problem of anomalies and accidents in coal mines due to coal fires and explosions. This results in loss of life due to the lack of automated alarming systems. We propose Thresh-Learner, a general purpose implementation for setting dynamic thresholds. We illustrate it through the Smart Helmet for coal mine workers which seamlessly integrates monitoring, analyzing and dynamic thresholds using IoT and analysis on the cloud.

scholarly journals The Relationship between Electric Power Consumption, Foreign Direct Investment and Economic Growth in Sri Lanka

2020 ◽  
pp. 21-31
Author(s):  
S. Maheswaranathan
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Sri Lanka ◽  
Direct Investment ◽  
Time Series Data ◽  
Empirical Finding ◽  
Electricity Consumption ◽  
Series Data ◽  
Long Run ◽  
Bound Test

Purpose: This paper investigates the long run relationship between electricity consumption, foreign direct investment and economic growth in Sri Lanka. Design/Methodology/Approach: The annual time series data over the period 1970–2017 is considered to this study. Augmented Dickey–Fuller (ADF) unit root analysis is employed for examining the stationary properties of the variables. Consequently, Autoregressive Distributed Lag (ARDL) analysis is employed to examining the short- run and long-run relationship between electricity consumption, foreign direct investment and economic growth in Sri Lanka. Further, this study used the diagnostic tests such as the residual normality test, heteroskedasticity and serial autocorrelation tests for misspecification to validate the parameter estimation outcomes achieved by the estimated model. CUSUM test is applied to test the stability of the model. Collected data were analyzed using STATA version 15. Findings: The findings of the bound test confirm that the variables are cointegrated. Further the results reveal that there is a statistically positive significant relationship between electricity consumption, foreign direct investment and economic growth in Sri Lanka in the long run and short term. The empirical finding reveals that one percent increase in electricity consumption and foreign direct investment increases the GDP by 1.5 percent and 12.9 percent in the long run respectively.

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