scholarly journals Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Muhammad Fahim ◽  
Alberto Sillitti
Keyword(s):  
Energy Consumption ◽  
Time Series Data ◽  
Residential Buildings ◽  
Series Data ◽  
Smart Meters ◽  
Learning Mechanism ◽  
Efficient Energy ◽  
Household Characteristics ◽  
Univariate Time Series ◽  
Consumption Data

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics.

Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing

2015 ◽  
pp. 320-333 ◽  
Author(s):  
Mariya Sodenkamp ◽  
Konstantin Hopf ◽  
Thorsten Staake
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Classification Accuracy ◽  
Residential Buildings ◽  
Supervised Machine Learning ◽  
Household Characteristics ◽  
Consumption Data ◽  
Feature Filtering ◽  
Electricity Load ◽  
Class Labels

Smart electricity meters allow capturing consumption load profiles of residential buildings. Besides several other applications, the retrieved data renders it possible to reveal household characteristics including the number of persons per apartment, age of the dwelling, etc., which helps to develop targeted energy conservation services. The goal of this chapter is to develop further related methods of smart meter data analytics that infer such household characteristics using weekly load curves. The contribution of this chapter to the state of the art is threefold. The authors first quadruplicate the number of defined features that describe electricity load curves to preserve relevant structures for classification. Then, they suggest feature filtering techniques to reduce the dimension of the input to a set of a few significant ones. Finally, the authors redefine class labels for some properties. As a result, the classification accuracy is elevated up to 82%, while the runtime complexity is significantly reduced.

Market Size Growth Survival in Multi-Generation Technology Environment: A Predictive Review of the Indian Air-Conditioner and Refrigerator Industry

2019 ◽  
Vol 9 (5) ◽  
pp. 132-146
Author(s):  
Ayan Chattopadhyay ◽  
Somarata Chakraborty
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Market Size ◽  
Air Conditioner ◽  
Growth Trend ◽  
Sarima Model ◽  
Technology Products ◽  
Univariate Time Series ◽  
Size Growth ◽  
Generation Technology

The ensuing paper aims to explore the future growth pattern of the market size of air-conditioner and refrigerator industry in India. Though this industry has witnessed phenomenal growth in the past, with multi-generation technology products driving it, its growth has remained erratic in nature. This paper also ratifies if the industry would survive the existing market size growth trend. In this predictive assessment, univariate time series data of net sales, collected from CMIE, is used. The data, spreading across 14 years, have 56 observations and exhibit both trend and seasonality. Forecast of market size is made using the best model derived from comparative approaches that include SARIMA, triple exponential smoothing and neural network. SARIMA model is found to best fit the historical data for predictive purpose and the study outcome suggests market size to grow till 2020. Finally, Weibull’s function is used to analyze reliability of the forecast results which indicates diminishing trend of the market size growth. Finally, it is concluded that the current erratic nature of market size growth would disappear.

CAUSAL RELATIONSHIP OF ENERGY CONSUMPTION AND ECONOMIC GROWTH – A CASE STUDY OF PAKISTAN

2021 ◽  
Vol 9 (1) ◽  
pp. 139-164
Author(s):  
Saddam Hussain ◽  
Chunjiao Yu
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Inflation Rate ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Series Data ◽  
Relationship Of

This paper explores the causal relationship between energy consumption and economic growth in Pakistan, applying techniques of co-integration and Hsiao’s version of Granger causality, using time series data over the period 1965-2019. Time series data of macroeconomic determi-nants – i.e. energy growth, Foreign Direct Investment (FDI) growth and population growth shows a positive correlation with economic growth while there is no correlation founded be-tween economic growth and inflation rate or Consumer Price Index (CPI). The general conclu-sion of empirical results is that economic growth causes energy consumption.

scholarly journals PENGARUH KONSUMSI ENERGI DAN REZIM TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 401
Author(s):  
Zakiah Husna ◽  
Idris Idris
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Renewable Energy Consumption ◽  
Energy Regime ◽  
Negative Effect ◽  
Positive Effect

This study aims to determine the effect of energy consumption and regime on economic growth in Indonesia. The data used is secondary data in the form of time series data from 1988-2017, with documentation and library study data collection techniques obtained from relevant institutions and agencies. the variables used are economic growth (GDP), non-renewable energy consumption, renewable energy consumption and regime, the research methods used are: (1) Multiple Regression Analysis (OLS), (2) Classical Assumption Test results of research stating that: ( 1) non-renewable energy consumption has a positive effect on economic growth in Indonesia. (2) consumption of renewable energy has a positive effect on economic growth in Indonesia. (3) the energy regime has a negative effect on economic growth in Indonesia. (4) non-renewable energy consumption, renewable energy consumption and energy regime have a significant effect on economic growth in Indonesia. so only the energy regime has a negative effect on economic growth in Indonesia.

scholarly journals Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7590
Author(s):  
Adam Kula ◽  
Albert Smalcerz ◽  
Maciej Sajkowski ◽  
Zygmunt Kamiński
Keyword(s):  
Energy Consumption ◽  
Public Service ◽  
Residential Buildings ◽  
Sun Exposure ◽  
Building Energy ◽  
The West ◽  
Building Energy Management ◽  
Consumption Data ◽  
Office Rooms ◽  
The Impact

There are many papers concerning the consumption of energy in different buildings. Most describe residential buildings, with only a few about office- or public service buildings. Few articles showcase the use of energy consumption in specific rooms of a building, directed in different geographical directions. On the other hand, many publications present methods, such as machine learning or AI, for building energy management and prediction of its consumption. These methods have limitations and represent a certain level of uncertainty. In order to compare energy consumption of different rooms, the measurements of particular building-room parameters were collected and analyzed. The obtained results showcase the effect of room location, regarding geographical directions, for the consumption of energy for heating. For south-exposed rooms, due to sun radiation, it is possible to switch heating off completely, and even overheating of 3 °C above the 22 °C temperature set point occurs. The impact of the sun radiation for rooms with a window directed east or west reached about 1 °C and lasts for a few hours before noon for the east, and until late afternoon for the west.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

Exploratory Time Series Data Mining by Genetic Clustering

2011 ◽  
pp. 157-178
Author(s):  
T. Warren Liao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measures ◽  
Series Data ◽  
Synthetic Control ◽  
Data Set ◽  
Univariate Time Series ◽  
Genetic Clustering ◽  
Data Objects

In this chapter, we present genetic algorithm (GA) based methods developed for clustering univariate time series with equal or unequal length as an exploratory step of data mining. These methods basically implement the k-medoids algorithm. Each chromosome encodes in binary the data objects serving as the k-medoids. To compare their performance, both fixed-parameter and adaptive GAs were used. We first employed the synthetic control chart data set to investigate the performance of three fitness functions, two distance measures, and other GA parameters such as population size, crossover rate, and mutation rate. Two more sets of time series with or without known number of clusters were also experimented: one is the cylinder-bell-funnel data and the other is the novel battle simulation data. The clustering results are presented and discussed.

scholarly journals The Financial Development-Renewable Energy Consumption Nexus in the Case of Azerbaijan

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6265
Author(s):  
Shahriyar Mukhtarov ◽  
Sugra Humbatova ◽  
Natig Gadim-Oglu Hajiyev ◽  
Sannur Aliyev
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Financial Development ◽  
Time Series Data ◽  
Policy Implications ◽  
Small Samples ◽  
Series Data ◽  
Energy Prices ◽  
Renewable Energy Consumption

This article analyzed the relationship between financial development, renewable energy consumption, economic growth, and energy prices in Azerbaijan by employing time series data for the time span of 1993–2015. The autoregressive distributed lagged (ARDL) technique was applied in empirical estimations, because it performs better than all the alternative techniques in small samples, which was the case here in this article. The results of estimation found that there is a positive and statistically significant influence of financial development and economic growth on renewable energy consumption, whereas the prices of energy proxied by CPI have an adverse impact on renewable energy consumption in Azerbaijan. Also, estimation results demonstrated that a 1% rise in financial development, proxied by domestic credit as a percentage of GDP, and economic growth increase renewable energy consumption by 0.16% and 0.60%, respectively. The different financial development impacts on renewable energy consumption and related policy implications were also introduced.

scholarly journals Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chenxi Chen ◽  
Yang Song ◽  
Xianbiao Hu ◽  
Ivan G. Guardiola
Keyword(s):  
Energy Consumption ◽  
Kansas City ◽  
Time Series Data ◽  
Principal Component ◽  
Handling Time ◽  
Series Data ◽  
Behavior Patterns ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Ev Charging

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

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