Sharif Digital Repository

Accurate monthly electricity consumption forecasting (ECF) can help retailers enhance the profitability in deregulated electricity markets. Most current methods use monthly load data to perform monthly ECF, which usually produces large errors due to insufficient training samples. A few methods try to use fine-grained smart-meter data (e.g., hourly data) to increase training samples. However, such methods still exhibit low accuracy due to the increase in forecasting steps. © 1975-2012 IEEE  

With proliferation of single-phase rooftop photovoltaic (PV) panels, phase balancing in low voltage (LV) distribution feeders becomes the point of concern. In this way, identification of the hosting phase of connected single-phase customers and PV panels is a prerequisite. This paper proposes an optimization model for the phase identification problem. The objective is to minimize the summation of the absolute error between estimated and measured variables. Smart meters (SMs) data including active and reactive power absorptions/injections, nodal voltage magnitudes, and network configuration data form the input of the model. Potential errors in the input data are captured in the model while... 

Implementation of various incentive-based and time-based load management strategies has great potential to decrease peak load growth and customer electricity bill cost. In recent years, developments in Plug in Hybrid Electric Vehicles (PHEVs) have provided various environmental and economic advantages. However, high penetration of electric vehicles in to the grid may cause high peak loads at different times of the days. Using advanced metering and automatic chargers makes it possible to optimize the charging cost, and release generation capacities to provide sustainable electricity supply. Using an appropriate encouraging program is a simple way for vehicle owners to manage their energy... 

Aggregating fine-granular data measurements from smart meters presents an opportunity for utility companies to learn about consumers' power consumption patterns. Several research studies have shown that power consumption patterns can reveal a range of information about consumers, such as how many people are in the home, the types of appliances they use, their eating and sleeping routines, and even the TV programs they watch. As we move toward liberalized energy markets, many different parties are interested in gaining access to such data, which has enormous economical, societal, and environmental benefits. However, the main concern is that many such beneficial uses of smart meter big data... 

Nowadays, with the increasing use of renewable energies in low voltage (LV) feeders, phase balancing research areas are of great importance. However, the lack of information about the hosting phase of customers and renewable sources is the missing link in such researches. To address this barrier, this paper proposes a mixed integer linear programming (MILP) method to identify the hosting phase of customers as well as renewable energies, such as photovoltaic (PV) panels. The model considers potential error in the input data. To overcome the complexity caused by data error, the input data in several time intervals are taken into account by the model. The model solves the phase identification... 

The development of smart meters enables situational awareness in electric power distribution systems. The situational awareness provides significant advantages such as line outage and electricity theft detection. This paper aims at using smart meter data to detect these anomalies. To do so, an appropriate cluster-based method as an unsupervised machine learning approach is applied. A stochastic method based on conditional correlation is also proposed to localize the anomalies. It is shown that this can be done by detecting changes in bus connections using present and historical smart meter data. Therefore, network topology inspection can be avoided if the proposed method is applied. A... 

Development of Smart Grid and deployment of smart meters in large scale has raised a lot of concerns regarding customers’ privacy. Consequently, several schemes have been proposed to overcome the above mentioned issue. These schemes mainly rely on data aggregation as a method of protecting users’ privacy from the grid operators. However, the main problem with most of these schemes is the fact that they require a large amount of processing power at the meter side. This, together with the fact that smart meters don't usually have a powerful processor, can cause the unavailability of smart meter data at the required time for operators of the grid, and at the same time prevents smart meters from... 

In this paper we evaluate the performance of Energy Management and Building Automation Systems in smart environment using wireless sensor network. We describe some aspects of smart grid and then explain the related communication models of smart meters and building management or energy management systems in a smart home. We propose three models, then we compare these models and we select a model for our test bed. We implement the selected model in real environment using wireless sensor network. We analyse network delay to find out whether wireless sensor network is useful or not. With proposed model, we can save up to 57% in energy cost. Also, we develop In- home display to inform customers...