Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Intelligent methods for load forecasting

Load forecasting is an important task in the management of a power utility. The most recent developments in forecasting involve the use of artificial intelligence techniques, which offer powerful modelling capabilities. This paper discusses these techniques and provides a review of their application to load forecasting.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

A refined parametric model for short term load forecasting

Abstract We present a refined parametric model for forecasting electricity demand which performed particularly well in the recent Global Energy Forecasting Competition (GEFCom 2012). We begin by motivating and presenting a simple parametric model, treating the electricity demand as a function of the temperature and day of the data. We then set out a series of refinements of the model, explaining the rationale for each, and using the competition scores to demonstrate that each successive refinement step increases the accuracy of the model’s predictions. These refinements include combining models from multiple weather stations, removing outliers from the historical data, and special treatments of public holidays.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

Spatial load forecasting using a demand propagation approach

A method for spatial electric load forecasting using multi-agent systems, especially suited to simulate the local effect of special loads in distribution systems is presented. The method based on multi-agent systems uses two kinds of agents: reactive and proactive. The reactive agents represent each sub-zone in the service zone, characterizing each one with their corresponding load level, represented in a real number, and their relationships with other sub-zones represented in development probabilities. The proactive agent carry the new load expected to be allocated because of the new special load, this agent distribute the new load in a propagation pattern. The results are presented with maps of future expected load levels in the service zone. The method is tested with data from a mid-size city real distribution system, simulating the effect of a load with attraction and repulsion attributes. The method presents good results and performance. © 2011 IEEE.

Considering Urban Dynamics in spatial electric load forecasting

When dealing with spatio-temporal simulations of load growth inside a service zone, one of the most important problems faced by a Distribution Utility is how to represent the different relationships among different areas. A new load in a certain part of the city could modify the load growth in other parts of the city, even outside of its radius of influence. These interactions are called Urban Dynamics. This work aims to discuss how to implement Urban Dynamics considerations into the spatial electric load forecasting simulations using multi-agent simulations. To explain the approach, three examples are introduced, including the effect of an attraction load, the effect of a repulsive load, and the effect of several attraction/repulsive loads at the same time when considering the natural load growth. © 2012 IEEE.

Short-term Load Forecasting Using Neural Network For Future Smart Grid Application

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.