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The chief aim of this paper is to develop an effective approach to the issue of load profile clustering by applying Improved Replacement In Bee Optimization algorithm (IRIBO). While, intelligent metering solutions like Automated Meter Reading (AMR), Automated Meter Infrastructure (AMI) are in place to address the current issues prevailing in the domain of electricity markets, algorithm using Improved Replacement In Bee Optimization has been proved beneficial and uncomplicated to apply within a selective database. In this study Load Profile (LP) clustering distribution networks based on the shape of the load profile was studied for fitness function in the selected LP clustering. The results clearly indicate that LP clustering has advantages in providing metering solutions to consumers who do not possess digital metering which can be easily operated with trivial changes in the calibrations. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a methodology for the investigation of buildings’ electrical consumption through clustering techniques. [•] For this purpose the performance of different clustering algorithms are evaluated. [•] Detailed parameter optimization guidelines are proposed for the SOM algorithm. [•] We use as a test case a set of buildings of different utilization in a university campus. [•] The methodology is a useful tool for the load profiling analysis. [Copyright &y& Elsevier]

SUMMARY The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• A novel load profiling framework using deep clustering to for feature reduction. • Framework eliminates preprocessing in hybrid load profiling algorithms. • Autoencoders capture main non-linear features to separate different load groups. • Improved SI on real-world big data compared to classical clustering techniques. • Framework produces robust load profiles that describe distinct customer groups. [Display omitted] Load profiling is an essential step in several smart meter analytics tasks, such as forecasting and planning, that directly impact sustainable energy management. As most real-world smart meter data are unlabeled, the unsupervised learning clustering-driven load profiling approach aims to group related customers based on usage trends. However, due to the curse of dimensionality, conventional clustering algorithms perform poorly and often lead to inadequate load curves. The proposed load profiling framework's novelty is twofold. First, using an autoencoder the framework represents data as a hierarchy within the layers of the deep network, allowing for dimensionality reduction and highly nonlinear decision separation between clusters at the autoencoder bottleneck level. This is achieved by implementing autoencoder-based clustering that automatically converts smart meter data into more clustering-friendly representations that retain the original data characteristics. Second, the framework integrates dimensionality reduction and clustering into a single end-to-end unsupervised learning framework. The deep clustering framework is then tested on two real-world smart meter datasets, and the experimental results show that the proposed framework produces significantly better load profiles when compared to classical clustering algorithms as well as previous hybrid frameworks proposed in the literature. The paper concludes with a discussion of the results and a set of future directions for improving the load profiling task. [ABSTRACT FROM AUTHOR]

Abstract: Smart grids provide newer ways of energy production, transmission, and distribution. In a smart grid finer control of electrical devices in household and buildings are implemented to better manage energy demand and supply. However, this finer control commonly known as demand side management (DSM) requires extensive simulation at various levels before a DSM algorithm may actually be deployed in a real building or neighborhood. Since forecasting the energy usage behavior of myriad number of electrical devices is a difficult exercise, simulations are done to assess the effectiveness of a DSM algorithm. The problem with the state-of-the-art simulators is that each is designed for simulating electrical devices’ behavior under specific and limited settings. To this end, we present a highly configurable and extensible smart grid simulator (SGS) that is capable of simulating per-minute granularity of energy usage under numerous settings. Moreover, SGS is able to simulate behavior at four levels: electrical devices, households and buildings, neighborhoods and cities. Given different scenarios SGS can simulate relativistic behavior of energy usage at all four levels. [Copyright &y& Elsevier]

In order to enable the key applications provided by smart grid, a dynamic distribution system state estimator has to function properly giving the real time knowledge of a distribution network load profiles. However, due to unavailability of enough measurements and lagging communication of the measured data, the current system cannot readily access measurements in real time. Therefore a practical load modeling technique has to be applied to provide real time distribution system load profile estimates with the respective confidence intervals. Nevertheless, modeling individual customer loads is far statistically complicated and computationally prohibitive for real time application. The customer loads are not always normally distributed but rather a combination of normal distribution, lognormal distribution, generalized pareto and many others. In this study, models of primary heating type load classes are used to represent individual customers. A statistical formulation based on clustered customer groups' hourly consumption model is used to calculate aggregated substation loads and day ahead load forecasts with the respective confidence intervals. The stochasticity of customer loads when aggregated at distribution substation is investigated for four customer type load classes. [ABSTRACT FROM AUTHOR]

Abstract: The article is focused on evaluating the relevance of load profiling information in electrical load forecasting, using neural networks as the forecasting methodology. Different models, with and without load profiling information, were tested and compared, and, the importance of the different inputs was investigated, using the concept of partial derivatives to understand the relevance of including this type of data in the input space. The paper presents a model for the day ahead load profile prediction for an area with many consumers. The results were analyzed with a simulated load diagram (to illustrate a distribution feeder) and also with a specific output of a 60/15kV real distribution substation that feeds a small town. The adopted methodology was successfully implemented and resulted in reducing the mean absolute percentage error between 0.5% and 16%, depending on the nature of the concurrent methodology used and the forecasted day, with a major benefit regarding the treatment of special days (holidays). The results illustrate an interesting potential for the use of the load profiling information in forecasting. [Copyright &y& Elsevier]

Abstract: The recent technological developments monitoring the electricity use of small customers provides with a whole new view to develop electricity distribution systems, customer-specific services and to increase energy efficiency. The analysis of customer load profile and load estimation is an important and popular area of electricity distribution technology and management. In this paper, we present an efficient methodology, based on self-organizing maps (SOM) and clustering methods (K-means and hierarchical clustering), capable of handling large amounts of time-series data in the context of electricity load management research. The proposed methodology was applied on a dataset consisting of hourly measured electricity use data, for 3989 small customers located in Northern-Savo, Finland. Information for the hourly electricity use, for a large numbers of small customers, has been made available only recently. Therefore, this paper presents the first results of making use of these data. The individual customers were classified into user groups based on their electricity use profile. On this basis, new, data-based load curves were calculated for each of these user groups. The new user groups as well as the new-estimated load curves were compared with the existing ones, which were calculated by the electricity company, on the basis of a customer classification scheme and their annual demand for electricity. The index of agreement statistics were used to quantify the agreement between the estimated and observed electricity use. The results indicate that there is a clear improvement when using data-based estimations, while the new-estimated load curves can be utilized directly by existing electricity power systems for more accurate load estimates. [ABSTRACT FROM AUTHOR]

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBD's detection hitrate will increase from 3% to 60%. [ABSTRACT FROM AUTHOR]

Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent (or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the independent variables. [ABSTRACT FROM AUTHOR]

Protective relays are vital components of power systems. They are deployed on every piece of major equipment and are attached to every circuit breaker in the system. As microprocessor technology continues to expand, microprocessor-based protective relays will enjoy enhanced functionality. Load profiling, fault oscillographic waveform capture, and metering are some of the enhanced functionality available in microprocessor-based protective relays. This article shows how existing signal processing capabilities of protective relays can be used to intelligently monitor power quality (PQ). The definition of power quality, the available functions, and their application to utilities are examined. In particular, it is noted that those PQ events that industrial account managers are interested in (voltage sags, swells, interruptions) and additional events PQ engineers need (waveform capture, harmonics) can be gathered by protective relays. The article concludes with some field results from a microprocessor-based protection unit. While this article discusses the integration of power quality monitoring into protective relaying specifically, PQ monitoring is also integrated into other intelligent electronic devices (IEDs) that have access to the system voltages and currents such as switch controllers and meters [ABSTRACT FROM PUBLISHER]

This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands. [ABSTRACT FROM AUTHOR]

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers. [ABSTRACT FROM AUTHOR]

• A stock of buildings is analyzed to discover typical load profiles. • The daily load profiles are grouped with a "Follow the Leader" clustering algorithm. • A globally optimal decision tree is employed to develop a customer classifier. • The proposed classifier performs better than the baseline model of about 6%. • The classifier makes use of non-intrusive attributes gathered from energy bills. The recent increasing spread of Advanced Metering Infrastructure (AMI) has enabled the collection of a huge amount of building related-data which can be exploited by both energy suppliers and users to gain insight on energy consumption patterns. In this context, data analytics-based methodologies can play a key role for performing advanced characterization, benchmarking and classification of buildings according to their typical energy use in the time domain. Traditionally, energy customers are classified according to their building end-use category. However, buildings belonging to the same category can exhibit very different energy patterns making ineffective this kind of a-priori categorization. For this reason, load profiling frameworks have been developed in the last decade to identify homogenous groups of buildings with similar daily energy profiles. The present study proposes a non-intrusive customer classification process, which does not use as predictive attributes in-field load monitoring data for the classification of unknown customers, but rather monthly energy bills and additional information on customers' habits collected by means of a phone survey. The proposed classification process is developed by analysing hourly energy consumption data of 114 electrical customers of an Italian Energy Provider. The representative daily load profiles are grouped using the "Follow the Leader" clustering algorithm and a globally optimal decision tree is employed to build a supervised classification model. The model, compared to a baseline recursive partitioning tree, leads to an increase of accuracy of about 6%. Eventually, the procedure exploits energy bill data also for estimating the magnitude of typical load profiles. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia (KSA) has a large solar and wind energy resource. Through its Vision 2030 to exploit such resources, KSA is planning to install 9.5 GW of renewable energy power generation systems by 2030, through a mix of solar and wind energy. The government is planning to invest 109 billion US$ over the next 20 years for solar energy. The focus will be on solar photovoltaic (PV) and concentrated solar technologies at a national level. So far, the electricity demand in KSA is almost entirely dependent on fossil fuels for generating power. This paper addresses the potential to utilize the solar radiation resource at a different scale and reduce the power demand from the grid, bringing collateral benefits for householders and the government alike. The work presents the results from monitoring the electricity consumption of two typical domestic buildings (villas) in Jeddah, KSA. The electricity consumption observations were associated with indoor environmental conditions to study how and when cooling demand affects final demand. The study investigated options to serve the observed demand profile of the villas with simulated power generation from arrays of PV panels installed on two buildings' roofs. Finally, a model of dynamic solar radiation simulation was developed to assess the hourly electricity generation, and a cost-benefit analysis was conducted for different capacity PV systems scenarios. The results indicate that locally used rooftop PV output could reduce the household electrical demand from the grid by around 80% at the housing level and in combination with building refurbishment solutions, could result in additional energy savings. The economic analysis discusses the implications of a proposed feed-in tariff with the associated payback periods and ROI, as well as proposals for PV system deployment at a large scale on the roof of buildings in KSA. [ABSTRACT FROM AUTHOR]

Energy is indispensable to human existence, and its need is on the increase daily due to technological advancement. In recent times, there is sporadic increase in the development of modern buildings, and these buildings require a cost-effective and sustainable source of energy. Nigeria and some other African countries have major energy shortage issues due to the insufficient power generation and distribution facilities. This energy poverty creates a challenge for the growing population, and as such, it is vital to ensure that the energy supplied to a building is duly optimized and delivered cost effectively. In this study, an energy audit of an eight-floor multipurpose business complex was performed to determine the nature and type of loads within the building. Based on the load profile and energy consumption over a 10-year period, three alternative energy sources (national grid, diesel generator and photovoltaic [PV] system) were considered using various load-sharing ratios. The result reveals that though PV solar system is a renewable energy source that reduces the production of greenhouse gases generated by burning diesel, its continuous application in low-middle-income country like Nigeria may be challenged by the initial start-up capital which raises the cost of its unit energy. [ABSTRACT FROM AUTHOR]

Global climate change has driven many manufacturing organizations to consider renewable energy as an alternative source of power for their operations. However, the instability and unpredictability of renewable energy has made it difficult to design an optimal solution. This is particularly critical for food manufacturing which is constrained by timing and seasonable fluctuations. This paper describes a feasibility study of offsetting grid supplied power by local generation using a roof top photovoltaic system. The study shows that the company can significantly reduce their carbon emitting energy usage by a 100 kW photovoltaic solar system that helps to reduce the annual electricity consumption by 39%. The system sizing methodology has taken into account the load profile of the food manufacturing plant and seasonal solar energy availability with the constrain to consume all solar power immediately. [ABSTRACT FROM PUBLISHER]