Electricity Usage Data Research Articles

Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling

Improvements to the current generation and distribution of electricity via demand side management (DSM) and storage systems are prevalent facing increasing energy demand and environmental implications of electricity generation. In this paper, a multi-level optimization model, which incorporates energy demand scheduler (DS), energy storage (ES) and solar photovoltaic (PV) panels amongst households, was developed so as to lower the peak-to-average ratio (PAR) of energy demand and reduce electricity bills. This model consists of three levels: (1) household consumption optimization (solo opt) using convex programming, (2) grid consumption optimization (base opt) via a game-theoretic framework, and (3) ES/PV allocation optimization using genetic algorithm (GA opt). This framework searches for the optimal allocation of ES/PV in a heterogeneous residential population subdivided into consumer groups by household sizes and income levels. A case study was performed with model parameters determined by referencing state-averaged electricity bills and electricity usage data from Texas, US. The results showed that GA opt can achieve bills savings of ~11% and a PAR reduction from 1.53 to 1.30 by allocating a non-trivial optimal combination of ES/PVs to the households. Another GA opt approach was adopted by minimizing PAR and found that PAR can be effectively reduced from 1.53 to 1.00 with bills savings of ~4%. Most significantly, it was observed that the optimal allocation differs from the free market equilibrium due to positive externalities and synergies when combining DSM together with ES/PVs.

Energy Conservation at School and Home

We do not always think of the impact we are making on the climate every time we turn on a light, use a computer or watch T.V. For this research-informed action research project we studied how electricity usage can impact the climate. We conducted a mini correlational study at Erindale Secondary School to learn more about our peers’ electricity consumption. We asked our peers how many fluorescent light bulbs they use in their homes, and how often they turn off their lights when they leave the room. Our mini study reveals that the majority of boys and girls do not know how many light bulbs in their homes are energy efficient. In addition, equal proportion of girls and boys always or sometimes turn off the lights when they are not in use. For the action portion of our project, we organized the Earth Hour at our school to lower the energy consumption and to raise awareness about the importance of energy conservation. The Principal of our school also agreed to turn off the lights in the cafeteria every night to save more energy. We also challenged David Suzuki Secondary School in Mississauga to a friendly competition to see which school will save more energy. We still await the electricity usage data from the board to see who won the challenge.

An efficient aggregation scheme resisting on malicious data mining attacks for smart grid

In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature. In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary can infer a target user’s electricity usage data. When considering this attack, the majority of existing data aggregation schemes have one of the following two shortcomings. In one aspect, the schemes based on homomorphic encryption can output an accurate aggregation result, but most of them are vulnerable to this attack. In another aspect, the schemes based on differential privacy able to withstand this attack, but the random noises introduced prevent accurate aggregation results from being computed. In this paper, we propose a novel data aggregation scheme that is not only secure against the malicious data mining attack, but also capable of outputting an accurate aggregation result. Detailed security analyses indicate that the proposed scheme satisfies the desirable properties for privacy-preserving data aggregation in the smart grid, and the simulated results demonstrate that our proposed scheme enjoys low computation and communication overhead.

Penggunaan Algoritma Jaringan Syaraf Tiruan Propagasi Balik Pada Klasifikasi Data Penggunaan Daya Listrik

Electrical Energy has a large contribution to the operational costs that must be incurred. The selection of electrical equipment can be one alternative that might be implemented to reduce operational costs incurred. In its use sometimes users do not know any electrical equipment that uses high electrical power and low electrical power. Therefore a system was made to classify data on electric power usage. This data will be classified into four classes, such as: very efficient, efficient, quite efficient and wasteful. Data classification is done using a back propagation neural network algorithm. The training data set used is 190 data and the test data set is 30 data. Based on the training that has been done, the optimal parameters are learning rate 0.5, target error 0.001, max epoch 10000, and 25 hidden neurons. Tests show that the system is able to recognize data with an accuracy level of 96.67% and MSE of 0.03333. Of the 30 data that have been tested obtained 29 data in accordance with the target. Where the 29 data are classified into 4 classes, namely 9 data classes are very efficient, 6 data classes are efficient, 5 data classes are quite efficient and 9 data classes are wasteful. The results of this study can be concluded that the backpropagation neural network algorithm can be implemented to classify electrical power usage data.

Design and Application of Monitoring System for Electrical Energy based-on Internet of Things

The system was designed for electrical energy usage data that be obtained through detection device can beaccessed and processed from an application on a user’s smartphone. The detection device can notice electricalenergy in the power cord of the electronic device. It has a different function with power as 18 watts up to 77 watts.Detection of device and application in the system are well integrated because of status in application displays thesame result as the LED indicator on the detection device. The system has an accuracy of 1 Wh with an uncertaintyof 0.5 Wh. The result shows that monitoring duration for 60 minutes in accordance with the specification of theelectronic device being monitored.

Placement of Charging Infrastructures for EVs using K-Mean Algorithm and its Validation using Real Usage Data

Global climate change is affecting human life more seriously than ever before. Countries around the world have identified cars as a significant source of pollution, leading to an increased interest in eco-friendly automobiles. Electric vehicles (EVs), which are characteristically eco-friendly, have become a choice for future transportation system. In this paper, we proposed a strategy for determining the appropriate placement of large-scale charging stations using K-mean algorithm. Also, the initial results were validated utilizing actual electricity usage data from the existing chargers. Currently in Korea, a typical public charging station has two to three chargers to support EV users. As EVs become more popular, new problems arise, such as charger hopping and/or long waiting lines. In order to address these new issues, a large-scale charging station concept which houses more than ten chargers, is suggested. In doing so, a strategic approach for selecting close-to-ideal locations for the charging stations is introduced to maximize the charging station’s effectiveness. In this study, Jeju Island, Korea, which has many EVs, was used as a testbed. With the wealth of EV chargers and their usage data, initial validation of the proposed methodology was made possible. During the evaluation of the best possible locations for the largescale charging stations, we considered the locations of tourist attractions and convenient support facilities, as well as the population. After the evaluations, the proposed locations were validated using actual long-term charger usage data on Jeju Island. The demonstrated strategy for identifying appropriate locations for large-scale charging stations can be used by other tourist heavy islands or even small countries.

Wireless Home Energy Meter Manager using Android: A Preliminary study

Manual home energy meter reading and billing had caused inconvenience to the utility companies due to lack of manpower to read the energy meter at each household especially in the remote area, explains the increasing number of smart meter reader in the current market. Most of the smart meters in the market do not offer safety of privacy of consumers’ personal information since the data of electricity usage is being transferred digitally to the utility companies for more accurate bills calculation. Plus, the smart meter system is also a bit pricey to be installed in the rural area. Therefore, a private system that able to read energy consumption from a DC load and calculate its bill according to the tariff is proposed. Value of current is being obtained by using ACS712 current sensor. Hall circuit in the current sensor will converts magnetic field into a proportional voltage. The proposed system allows energy meter monitoring from an Android-based smartphone by displaying the real-time energy consumption and bill on Blynk application. An interface of Blynk is developed and connected to WiFi module, ESP8266 for visualizing the energy consumption of the DC load. In conclusion, the Energy Meter transmitter part able to read, calculate and transmit value of energy consumption and current bills to the Blynk application and Blynk application able to receive and show all the data transmitted at the present time. This system will be further improved for long-distance monitoring of electrical appliances used at home.

Algorithmic analysis of intelligent electricity meter data for reduction of energy consumption and carbon emission

Intelligent Electricity Meters (IEMs) generate a considerable amount of household electricity usage data incrementally. Obviously, for the clustering task, it is better to incrementally update the new clustering results based on the old data rather than to recluster all the data from scratch. The gradational clustering is an essential way to accommodate the influx of new data seamlessly for accurate analysis. However, given the volume of IEM data and the number of data types involved makes the gradational clustering highly complex. Microsoft Azure provides the processing power necessary to handle gradational clustering analytics. The paper aim is to develop a Distributed Log-likelihood Based Gradational Clustering Algorithm on Microsoft Azure for analysis of IEM data. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data, including the geographic information, demographic data. It is visible from the study that algorithmic analysis helps the household customers to monitor and improvise electricity consumption patterns, Utility providers to reduce power outage and avoid capital expenses of building new plants. This research will be extremely useful for maintaining the environment by reducing pollution via carbon production by power plants.

Carbon footprint assessment at Universitas Pertamina from the scope of electricity, transportation, and waste generation: Toward a green campus and promotion of environmental sustainability

Abstract A carbon footprint is the total amount of greenhouse gas emissions generated directly or indirectly from the daily routine of human activities and is usually expressed by carbon dioxide equivalent (CO2e). As a new educational institution, Universitas Pertamina began to take part in an Intergovernmental Panel on Climate Change (IPCC) program that supports the reduction of carbon emissions. The first step was to identify the amount of carbon emissions produced by members at Universitas Pertamina. The initiative to calculate carbon emissions is a new breakthrough step for newly launched universities. This study measures the total carbon emission generated at the university from the scope of electricity, transportation, and waste generation. Basic data were obtained through direct sampling, questionnaire survey, and secondary data, particularly data of electricity usage. The results showed that electricity is the greatest contributor of carbon emissions at the university, at 92.3%, followed by transportation at 6.66% and waste generation at 1.04%. The total amount of carbon dioxide (CO2) emission from three different sources within one year was 1,351.98 metric tons of carbon dioxide (MTCO2), which is equal to 0.52 MTCO2/person/year. The basis data of the carbon footprint at Universitas Pertamina will become a baseline for monitoring, evaluating, and target setting for programs aimed to reduce greenhouse gas emissions.

Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea

Renewable energy sources such as solar panels with energy storage, ground-source heat pumps, and fuel cells are increasingly being integrated into buildings to improve their sustainability. This paper investigates, for an urban area, the effects of building-integrated renewable energy on the building energy portfolio and electricity & gas flows from the grid. In this study, 403 apartment buildings and 269 non-residential buildings (with a floor area over 500 m2) in a district of Seoul, Korea, are assumed to integrate renewable energy sources to satisfy the renewable energy requirements of 10% and 30% of the total energy demand, respectively. The optimal renewable energy system for each building has been obtained via Linear Programming. The optimization has been carried out based on the hourly electricity, heating, and cooling loads of every building over a 1-year period. The energy loads, in turn, have been derived from the monthly electricity and gas usage data for every building. The renewable energy portfolio for the apartment buildings consists of photovoltaics, fuel cells, and solar water heaters. About 74% of the renewable energy for apartment buildings is provided by photovoltaics. The renewable energy portfolio for the non-residential buildings consists of ground-source heat pumps and photovoltaics. About 71% of the renewable energy is provided by the ground-source heat pumps. Consequently, total electricity and gas supply from the grid to all of the buildings in the district decrease by 17% and 3%, respectively. The photovoltaics induce a large variation in power flow from the grid during the daytime (i.e., the so-called Duck curve). The annual peak power occurs in the winter mornings because electricity powered heating by ground-source heat pumps replaces gas heating, and maximum heat demand occurs in winter mornings.

A Physically Inspired Data-Driven Model for Electricity Theft Detection With Smart Meter Data

Electricity theft is the third largest form of theft in the United States. It not only leads to significant revenue losses, but also creates the risk of fires and fatal electrical shocks. In the past, utilities have fought electricity theft by sending field operation groups to conduct physical inspections of electrical equipment based on suspicious activity reported by the public. However, the recent rapid penetration of advanced metering infrastructure makes it possible to detect electricity theft by analyzing the information gathered from smart meters. In this paper, we develop a physically inspired data driven model to detect electricity theft with smart meter data. The main advantage of the proposed model is that it only leverages the electricity usage and voltage data from smart meters instead of unreliable parameter and topology information of the secondary network. Hence, a speedy and widespread adoption of the proposed model is feasible. We show that a modified linear regression model accurately captures the physical relationship between electricity usage and voltage magnitude on the Kron-reduced distribution secondaries. Our results show that electricity theft on a distribution secondary will lead to negative and positive residuals from the regression for dishonest and honest customers, respectively. The proposed model is validated with real-world smart-meter data. The results show that the model is effective in identifying electricity theft cases.

A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis

The problem of customizing electricity retail prices using data mining techniques is studied in this paper. The density-based spatial clustering of applications with noise is first applied to load profile analysis, in order to explore end-users’ inherent electricity consumption patterns from their historical load data. Then, statistical analysis of end-users’ historical consumption is conducted to better capture their consumption regularity. After extracting these load features, a mixed integer nonlinear programming model for customizing electricity retail prices is proposed. In the proposed model, both the structure of time-of-use (TOU) retail price and the price level are optimized once given the number of price blocks. It is among the first that the optimization of TOU price structure is studied in electricity retail pricing research. The proposed model is mathematically reformulated and solved by online commercial solvers provided by the network-enabled optimization system server. Electricity usage data collected by the smart grid, smart city project in Australia is used to demonstrate the feasibility and efficiency of the developed models and algorithms.

An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings

Abstract This study presents an agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. The proposed strategy takes the advantages of three dissimilarity measures (i.e. Euclidean distance, Pearson distance and Chebyshev distance) to calculate the difference between daily electricity usage profiles. Two-year hourly electricity usage data collected from two different university library buildings were employed to evaluate the performance of this strategy. It was shown that this strategy, which considered both magnitude dissimilarity and variation dissimilarity simultaneously, can identify more informative typical daily electricity usage profiles, in comparison with other twelve clustering-based strategies which used a single dissimilarity measure. Some interesting information related to building energy usage behaviours was also discovered with the help of visualisation techniques. Additional or hidden information discovered using this strategy can potentially be useful for fault detection and diagnosis and performance enhancement of library buildings.

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large-scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4-bus and 33-bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity.

Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior.

An efficient heterogeneous signcryption for smart grid.

A smart grid, considered the next-generation type of power grid, combines a traditional power grid with information and communication technologies to effectively facilitate power generation and ensure transmission security and reliability in real-time. Only authorized consumers should be able to access the smart grid because the information gathered by smart meters includes users’ private information. However, smart grid security is still a challenge. Motivated by this challenge, in this paper, we propose a heterogeneous signcryption (HSC) scheme for secure communication between smart meters and the utility. We demonstrate that this scheme is indistinguishable against adaptive chosen-ciphertext attacks (IND-CCA2), existentially unforgeable against adaptive chosen-message attacks (EUF-CMA) and ciphertext-anonymous against adaptive chosen ciphertext attacks (ANON-CCA2) under the computational Diffie-Hellman (CDH) problem in the random oracle model. Our scheme simultaneously achieves confidentiality, integrity, authentication, non-repudiation and ciphertext anonymity in a single logical step. It supports heterogeneous systems, allowing a meter in an identity-based cryptography (IBC) environment to transmit electrical usage data to a utility in a public key infrastructure (PKI) environment. Compared with other existing related schemes, our scheme has the lowest communication overhead and energy consumption for the smart grid. Based on these features, our scheme is highly suitable for secure power transmissions in a smart grid.

A PLC Channel Model for Home Area Networks

Smart meters (SMs) are key components of the smart grid (SG) which gather electricity usage data from residences and businesses. Home area networks (HANs) are used to support two-way communications between SMs and devices within a building such as appliances. This can be implemented using power line communications (PLCs) via home wiring topologies. In this paper, a bottom-up approach is designed and a HAN-PLC channel model is obtained for a split-phase power system which includes branch circuits, an electric panel with circuit breakers and bars, a secondary transformer and the wiring of neighboring residences. A cell division (CD) method is proposed to construct the channel model. Furthermore, arc fault circuit interrupter (AFCI) and ground fault circuit interrupter (GFCI) circuit breaker models are developed. Several HAN-PLC channels are presented and compared with those obtained using existing models.

Application of Linear Regression Analysis Model on Early Warning System for Inefficiency of Electricity Usage

Recently the Indonesian people often get inefficiency of electricity usage. On the other side, in Indonesia, the electricity is mostly produced from steam power plant, which require fuel from non-renewable natural resources. So the highness of demand and the occurrence of inefficiency the electricity usage can increase the consumption of natural resource and the air pollution. Therefore, an early warning system are proposed in this study, become one of the various solution than can increase awareness of the people in efficiency of electricity usage. This system requires the input data of electricity usage in the last 6 months, that will be formed the electricity usage trend from each user using linear regression analysis. Furthermore, this trend will predict the electricity usage for next month, this is used as the limit to give the warning from the system. The outcome from this study is the system that can provide a warning to users if their electricity usage run over the certain limits.

Stochastic load profile construction for the multi-tier framework for household electricity access using off-grid DC appliances

To improve access to electricity, decentralized, solar-based off-grid solutions like Solar Home Systems (SHSs) and rural micro-grids have recently seen a prolific growth. However, electrical load profiles, usually the first step in determining the electrical sizing of off-grid energy systems, are often non-existent or unreliable, especially when looking at the hitherto un(der)-electrified communities. This paper aims to construct load profiles at the household level for each tier of electricity access as set forth by the multi-tier framework (MTF) for measuring household electricity access. The loads comprise dedicated off-grid appliances, including the so-called super-efficient ones that are increasingly being used by SHSs, reflecting the off-grid appliance market’s remarkable evolution in terms of efficiency and price. This study culminated in devising a stochastic, bottom-up load profile construction methodology with sample load profiles constructed for each tier of the MTF. The methodology exhibits several advantages like scalability and adaptability for specific regions and communities based on community-specific measured or desired electricity usage data. The resulting load profiles for different tiers shed significant light on the technical design directions that current and future off-grid systems must take to satisfy the growing energy demands of the un(der)-electrified regions. Finally, a constructed load profile was also compared with a measured load profile from an SHS active in the field in Rwanda, demonstrating the usability of the methodology.

Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms

In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to extract important features to reveal households’ characteristics based on electricity usage data. The main goal of the analysis is to automatically extract, in a non-intrusive way, number of socio-economic household properties including family type, age of inhabitants, employment type, house type, and number of bedrooms. The knowledge of specific properties enables energy utilities to develop targeted energy conservation tariffs and to assure balanced operation management. In particular, classification of the households based on the electricity usage delivers value added information to allow accurate demand planning with the goal to enhance the overall efficiency of the network. The approach was evaluated by analyzing smart meter data collected from 4182 households in Ireland over a period of 1.5 years. The analysis outcome shows that revealing characteristics from smart meter data is feasible, and the proposed machine learning methods were yielding for an accuracy of approx. 90% and Area Under Receiver Operating Curve (AUC) of 0.82.