Disaggregated Electricity Bill Base on Utilization factor and Time-of-use (ToU) Tariff

2017 ◽  
Vol 7 (3) ◽  
pp. 1498
Author(s):  
Nur Farahin Asa @ Esa ◽  
Md Pauzi Abdullah ◽  
Mohammad Yusri Hassan ◽  
Faridah Hussin
Keyword(s):  
Electricity Consumption ◽  
Consumption Pattern ◽  
Usage Pattern ◽  
Profile Data ◽  
Utilization Factor ◽  
Load Profile ◽  
Electricity Bill ◽  
Usage Behavior ◽  
Usage Patterns

Time of Use tariff is introduced to motivate users to change their electricity usage pattern. Commonly the tariff is high during peak hours and relatively low during off peak hours, to encourage users to reduce consumption during peak hours or shift it to off-peak hours. This tariff scheme provides opportunities for building owners to reduce their electricity bill provided that their electricity usage patterns of various spaces in that building at every hour are known. In practice, the kWh meter installed by the utility can only provide the overall hourly electricity consumption pattern. To know the usage pattern of different spaces or rooms, separate individual meter need to be installed in each space/room, which is costly and impractical.  This paper presented the disaggregated electricity bill method based on user utilization factor and time of use (ToU) tariff. It estimates hourly electricity bill of each appliance at each space/room. Utilization factor is used to represent the electricity usage behavior of the occupants. The proposed method is applied on practical load profile data of a university building.

scholarly journals Segmentation of Factories on Electricity Consumption Behaviors Using Load Profile Data

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 8394-8406 ◽  
Author(s):  
Imran Khan ◽  
Joshua Zhexue Huang ◽  
Md Abdul Masud ◽  
Qingshan Jiang
Keyword(s):  
Electricity Consumption ◽  
Profile Data ◽  
Load Profile

scholarly journals Maximizing Savings in Electricity Bills Based on Customer’s Load Profile

2019 ◽  
Vol 8 (4) ◽  
pp. 6542-6546
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Weather Condition ◽  
Electricity Demand ◽  
Economic Feasibility ◽  
Solar Pv ◽  
Load Profile ◽  
Electricity Bill ◽  
Number Of Customers

With the high demand in electricity consumption nowadays, it is crucial for regulator and utilities to ensure sufficient energy supply to meet electricity demand. Electricity demand is influenced by several factors such as number of customers, customer behavior, working hours, weather condition and holidays. Integrating renewable energy technology as part of electricity generation for self consumption has indirectly provide an option to customer to reduce his electricity consumption from the grid and help to save his electricity bill. One of the simplest solutions to install renewable energy sources is by installing rooftop solar photovoltaic (PV). In this paper, the economic feasibility of installing solar PV is discussed based on commercial customer load profile. This paper also presents the suitable PV sizing based on the payback analysis based on customer load profile. A commercial customer in Petaling Jaya, Selangor is used as a case study for this analysis. This study indicates that customer will be able to reduce their electricity bill consumption with the integration of solar PV system on the rooftop of their building. Customer is able to save their monthly electricity up to 28% if a total solar PV capacity of 1256kW is installed. The payback from this study indicates the payback period is approximately around 9 years

scholarly journals Feasibility Study of the Technical and Economic Performance of Grid-Connected PV System for Selected Rooftops in UTHM

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Mohamad Fakrie Mohamad Ali ◽  
◽  
Mohd Noor Abdullah ◽  
Keyword(s):  
Feasibility Study ◽  
Crystalline Silicon ◽  
Electricity Consumption ◽  
Payback Period ◽  
Net Energy ◽  
Pv System ◽  
Pv Module ◽  
Electricity Bill ◽  
Pv Modules ◽  
Energy Metering

This paper presents the feasibility study of the technical and economic performances of grid-connected photovoltaic (PV) system for selected rooftops in Universiti Tun Hussein Onn Malaysia (UTHM). The analysis of the electricity consumption and electricity bill data of UTHM campus show that the monthly electricity usage in UTHM campus is very high and expensive. The main purpose of this project is to reduce the annual electricity consumption and electricity bill of UTHM with Net Energy Metering (NEM) scheme. Therefore, the grid-connected PV system has been proposed at Dewan Sultan Ibrahim (DSI), Tunku Tun Aminah Library (TTAL), Fakulti Kejuruteraan Awam dan Alam Bina (FKAAS) and F2 buildings UTHM by using three types of PV modules which are mono-crystalline silicon (Mono-Si), poly-crystalline silicon (Poly-Si) and Thin-film. These three PV modules were modeled, simulated and calculated using Helioscope software with the capacity of 2,166.40kWp, 2,046.20kWp and 1,845kWp respectively for the total rooftop area of 190,302.9 ft². The economic analysis was conducted on the chosen three installed PV modules using RETScreen software. As a result, the Mono-Si showed the best PV module that can produce 2,332,327.40 kWh of PV energy, 4.4% of CO₂ reduction, 9.3 years of payback period considering 21 years of the contractual period and profit of RM4,932,274.58 for 11.7 years after payback period. Moreover, the proposed installation of 2,166.40kWp (Mono-SI PV module) can reduce the annual electricity bill and CO2 emission of 3.6% (RM421,561.93) and 4.4% (1,851.40 tCO₂) compared to the system without PV system.

scholarly journals Photovoltaic Systems and Net Metering in Greece

2018 ◽  
Vol 8 (4) ◽  
pp. 3168-3171
Author(s):  
F. Mavromatakis ◽  
G. Viskadouros ◽  
H. Haritaki ◽  
G. Xanthos
Keyword(s):  
Net Present Value ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Internal Rate Of Return ◽  
Pv System ◽  
Local Loads ◽  
Electricity Bill ◽  
Net Metering ◽  
Cost Of Energy ◽  
Electrical Consumption

The latest measure for the development of photovoltaics in Greece utilizes the net-metering scheme. Under this scheme the energy produced by a PV system may be either consumed by the local loads or be injected to the grid. The final cost reported in an electricity bill depends upon the energy produced by the PV system, the energy absorbed from the grid and the energy injected to the grid. Consequently, the actual electricity consumption profile is important to estimate the benefit from the use of this renewable energy source. The state latest statistics in Greece for households reveal that the typical electrical consumption is 3750 kWh while 10244 kWh are consumed in the form of thermal energy. We adopt in our calculations the above amount of electrical energy but assume four different scenarios. These different hourly profiles are examined to study the effects of synchronization upon the final cost of energy. The above scenarios are applied to areas in different climate zones in Greece (Heraklion, Athens and Thessaloniki) to examine the dependence of the hourly profiles and the solar potential upon the financial data with respect to internal rate of return, payback times, net present value and the levelized cost of energy. These parameters are affected by the initial system cost and the financial parameters.

Electricity Usage Monitoring Based On Internet of Things

2021 ◽  
Vol 20 (3) ◽  
pp. 37-42
Author(s):  
Mohd Ridzuan Ahmad ◽  
Hishamuddin Hashim
Keyword(s):  
Internet Of Things ◽  
Electricity Consumption ◽  
Current Transformer ◽  
Consumption Pattern ◽  
Process Scheduling ◽  
Time Data ◽  
Domestic Use ◽  
Electrical Consumption ◽  
The Internet Of Things ◽  
A Current

Electricity monitoring systems have long been used in industrial scenarios such as process scheduling and distribution. This monitoring system needs to be developed for domestic use such as in homes and shops. In recent times, the electricity demand has increased in households with the use of different appliances. The advent of technologies such as the Internet of Things (IoT) has made real-time data acquisition and analysis possible. This project is designed to control and monitor household electricity consumption via smartphones using the ESP8266 Wi-Fi module as a communication protocol and the Blynk application as a private server. The used wifi module provides notification through the Blynk application. The system uses an Arduino Mega2560 microcontroller to control all devices in this project. For monitoring the energy usage, a current sensor type Split Core Current Transformer (SCT013) was used. From the experimental results, it is confirmed that the system is capable of monitoring the whole house’s electrical usage easily. With this system in place, end-users are provided with proper awareness and able to plan their home’s electrical consumption pattern effectively.

Residential Load Pattern Analysis for Smart Grid Applications Based on Audio Feature EEUPC

2013 ◽  
pp. 107-121
Author(s):  
Yunzhi Wang ◽  
Xiangdong Wang ◽  
Yueliang Qian ◽  
Haiyong Luo ◽  
Fujiang Ge ◽  
...  
Keyword(s):  
Smart Grid ◽  
Clustering Analysis ◽  
Pattern Analysis ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Typical Pattern ◽  
Audio Feature ◽  
Load Pattern ◽  
Grid Applications

The smart grid is an important application field of the Internet of things. This paper presents a method of user electricity consumption pattern analysis for smart grid applications based on the audio feature EEUPC. A novel similarity function based on EEUPC is adapted to support clustering analysis of residential load patterns. The EEUPC similarity exploits features of peaks and valleys on curves instead of directly comparing values and obtains better performance for clustering analysis. Moreover, the proposed approach performs load pattern clustering, extracts a typical pattern for each cluster, and gives suggestions toward better power consumption for each typical pattern. Experimental results demonstrate that the EEUPC similarity is more consistent with human judgment than the Euclidean distance and higher clustering performance can be achieved for residential electric load data.

Economically Optimal Solar Power Generation

2020 ◽  
pp. 254-290
Author(s):  
Sana Badruddin ◽  
Cameron Ryan Robertson-Gillis ◽  
Janice Ashworth ◽  
David J. Wright
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Solar Power ◽  
Electricity Consumption ◽  
The Other ◽  
Electricity Prices ◽  
Electricity Grid ◽  
The Public ◽  
Solar Power Generation ◽  
Electricity Bill

The Ottawa Renewable Energy Cooperative is considering installing solar modules on the roofs of two buildings while they stay connected to the public electricity grid. Solar power produced over their own needs would be sent to the public electricity grid for a credit on their electricity bill. When they need more power than they are generating, these buildings would purchase electricity from the grid. In addition to paying for the electricity they purchase, they would be subject to a “demand charge” that applies each month to the hour during which their consumption is at a peak for that month. Any electricity consumed during that peak hour would be charged at a rate about 100 times the rate for other hours. The case addresses three questions: (1) Is it profitable for these organizations to install solar on their roofs? (2) Can profitability be increased by adding a battery? and (3) How sensitive is profitability to uncertainty in future electricity prices? The case shows how the answers to these questions depend on the profile of hourly electricity consumption during the day, which is very different from one building to the other.

Smartphone Data Protection Using Mobile Usage Pattern Matching

2011 ◽  
pp. 23-39
Author(s):  
Wen-Chen Hu ◽  
Naima Kaabouch ◽  
S. Hossein Mousavinezhad ◽  
Hung-Jen Yang
Keyword(s):  
Pattern Matching ◽  
Data Protection ◽  
Finite Automata ◽  
String Matching ◽  
Handheld Devices ◽  
Approximate String Matching ◽  
Usage Pattern ◽  
Usage Patterns ◽  
Novel Approaches

Handheld devices like smartphones must include rigorous and convenient handheld data protection in case the devices are lost or stolen. This research proposes a set of novel approaches to protecting handheld data by using mobile usage pattern matching, which compares the current handheld usage pattern to the stored usage patterns. If they are drastic different, a security action such as requiring a password entry is activated. Various algorithms of pattern matching can be used in this research. Two of them are discussed in this chapter: (i) approximate usage string matching and (ii) usage finite automata. The first method uses approximate string matching to check device usage and the second method converts the usage tree into a deterministic finite automaton (DFA). Experimental results show this method is effective and convenient for handheld data protection, but the accuracy may need to be improved.

scholarly journals Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4154 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Learning Strategy ◽  
Demand Management ◽  
Management Strategies ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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