A review and categorization of electric load forecasting techniques is presented. A wide range of methodologies and models for forecasting are given in the literature. These techniques are classified here into nine categories: (1) multiple regression, (2) exponential smoothing, (3) iterative reweighted least-squares, (4) adaptive load forecasting, (5) stochastic time series, (6) ARMAX models based on genetic algorithms, (7) fuzzy logic, (8) neural networks and (9) expert systems. The methodology for each category is briefly described, the advantages and disadvantages discussed, and the pertinent literature reviewed. Conclusions and comments are made on future research directions.

Electric load forecasting: Literature survey and classification of methods

The possibility of leveraging the data provided by smart meters to understand the load characteristics is studied in this paper. The loads are modeled as voltage-dependent elements to increase the accuracy of volt-VAR optimization (VVO) techniques for distribution systems. VVO techniques are part of the distribution management system and may be used for purposes such as loss reduction, voltage profile improvement, and conservation voltage reduction. A deterministic framework is proposed that formulates the VVO problem as a mixed-integer quadratically constrained programming problem, which is solved efficiently using advanced branch-and-cut techniques. The proposed framework is capable of optimally controlling capacitor banks, voltage regulators, and under-load tap changers (ULTCs) for day-ahead operation planning. The results indicate that loss reductions of up to 40% and a total demand reduction of up to 4.8% are achievable under some loading conditions in a radial test system. The effect of the load voltage dependence is also demonstrated through analytical simulations.

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A Framework for Volt-VAR Optimization in Distribution Systems

Electricity demand forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year. Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique.

Load forecasting techniques and methodologies: A review

In recent years, fuzzy set theory applications have received increasing attention in various areas of power systems such as operation, planning, and control. A number of research articles appeared which indicate applicability of fuzzy systems to power systems for wider operating conditions under uncertainties. While most of these systems are still under investigation, however, there already exist several practical applications of fuzzy systems. This paper presents a comprehensive set of references on fuzzy set theory applications in power systems.

G. Ramakrishna

Papers

Adaptive neuro-fuzzy inference system for volt/var control in distribution systems

Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface

Application of a neural network as an integrated excitation controller