Integrated ANN approach to forecast load
TL;DR: A short-term load-forecasting program that uses an integrated artificial neural network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information.
Abstract: The demand for electricity is known to vary by the time of the day, week, month, temperature, and usage habits of the consumers. Though usage habit is not directly observable, it may be implied in the patterns of usage that have occurred in the past. A short-term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. How well training data is chosen in an ANN is the defining factor in how well the network's output will match the event being modeled.
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TL;DR: A systematic and critical review of forecasting methods used in 483 EPMs, finding that computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data.
Abstract: Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs.
219 citations
15 Mar 2009
TL;DR: In this article, a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network is presented.
Abstract: Short-term load forecast is an essential part of electric power system planning and operation. Forecasted values of system load affect the decisions made for unit commitment and security assessment, which have a direct impact on operational costs and system security. Conventional regression methods are used by most power companies for load forecasting. However, due to the nonlinear relationship between load and factors affecting it, conventional methods are not sufficient enough to provide accurate load forecast or to consider the seasonal variations of load. Conventional ANN-based load forecasting methods deal with 24-hour-ahead load forecasting by using forecasted temperature, which can lead to high forecasting errors in case of rapid temperature changes. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. The suitability of the proposed approach is illustrated through an application to the actual load data of the Egyptian Unified System.
89 citations
06 May 2005
TL;DR: In this paper, an adaptive load shedding strategy by executing an artificial neural network (ANN) and transient stability analysis for an electric utility system is presented, where the Levenberg-Marquardt algorithm has been adopted and incorporated into the back-propagation learning algorithm for training feedforward neural networks.
Abstract: The design of an adaptive load-shedding strategy by executing an artificial neural network (ANN) and transient stability analysis for an electric utility system is presented. To prepare the training data set for an ANN, the transient stability analysis of an actual power system has been performed to solve for minimum load shedding with various operation scenarios without causing the tripping problem of generators. The Levenberg-Marquardt algorithm has been adopted and incorporated into the back-propagation learning algorithm for training feedforward neural networks. By selecting the total power generation, total load demand and frequency decay rate as the input neurons of the ANN, the minimum amount of load shedding is determined to maintain the stability of power systems. To demonstrate the effectiveness of the proposed ANN minimum load-shedding scheme, a utility power system has been selected for computer simulation and the amount of load shedding is verified by stability analysis.
85 citations
TL;DR: Several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field are introduced and a hybrid strategy is proposed.
Abstract: Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed.
85 citations
Cites background from "Integrated ANN approach to forecast..."
...Thus, a suitable architecture for a forecasting engine can provide a simpler model to decrease the number of redundant data [70]....
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21 Jun 2017
TL;DR: In this paper, a state-of-the-art review of three artificial intelligence techniques for short-term electric load forecasting is comprehensively presented, including artificial neural network, support vector machine, and adaptive neuro-fuzzy inference system.
Abstract: According to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such as climate factors, social activities, and seasonal factors. The methods developed for load forecasting are broadly analyzed in two categories, namely analytical techniques and artificial intelligence techniques. In the literature, commonly used analytical methods are linear regression method, Box-Jenkins method, and nonparametric regression method. The analytical methods work well under normal daily circumstances, but they can't give contenting results while dealing with meteorological, sociological or economical changes, hence they are not updated depending on time. Therefore, artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, support vector machine, and adaptive neuro-fuzzy inference system are among these artificial intelligence techniques. In this paper, a state-of-the-art review of three artificial intelligence techniques for short-term electric load forecasting is comprehensively presented.
71 citations
Additional excerpts
...In the STLF literature, there are various applications of artificial intelligent techniques which can be categorized as artificial neural network (ANN) [21]–[24], support vector machine (SVM) [25]–[28], adaptive neuro-fuzzy inference system (ANFIS) [29], [30], genetic algorithm (GA) [31]–[33], fuzzy logic (FL) [34]–[36], self-organizing map (SOM) [37], [38], and extreme learning machines (ELM) [39]–[41]....
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References
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Book•
01 Jan 1970
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Abstract: From the Publisher:
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.
19,748 citations
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
Abstract: time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time series analysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series analysis forecasting and control pambudi, time series analysis forecasting and control george e, time series analysis forecasting and control ebook, time series analysis forecasting and control 5th edition, time series analysis forecasting and control fourth, time series analysis forecasting and control amazon, wiley time series analysis forecasting and control 5th, time series analysis forecasting and control edition 5, time series analysis forecasting and control 5th edition, time series analysis forecasting and control abebooks, time series analysis for business forecasting, time series analysis forecasting and control wiley, time series analysis forecasting and control book 1976, time series analysis forecasting and control researchgate, time series analysis forecasting and control edition 4, time series analysis forecasting amp control forecasting, george box publications department of statistics, time series analysis forecasting and control london, time series analysis forecasting and control an, time series analysis forecasting and control amazon it, box g e p and jenkins g m 1976 time series, time series analysis forecasting and control pdf slideshare, time series analysis forecasting and control researchgate, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, time series wikipedia, time series analysis forecasting and control abebooks, time series analysis forecasting and control, forecasting and time series analysis using the sca system, time series analysis forecasting and control by george e, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, box and jenkins time series analysis forecasting and control, time series analysis forecasting and control ebook, time series analysis forecasting and control, time series analysis and forecasting cengage, 6 7 references itl nist gov, time series analysis forecasting and control george e, time series analysis and forecasting statgraphics, time series analysis forecasting and control fourth edition, time series analysis forecasting and control, time series analysis forecasting and control wiley, time series analysis forecasting and control in
10,118 citations
TL;DR: In this article, an artificial neural network (ANN) approach is presented for electric load forecasting, which is used to learn the relationship among past, current and future temperatures and loads.
Abstract: An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data. >
1,350 citations
01 Dec 1987
TL;DR: In this paper, the authors discuss the state of the art in short-term load forecasting (STLF), that is, the prediction of the system load over an interval ranging from one hour to one week.
Abstract: This paper discusses the state of the art in short-term load forecasting (STLF), that is, the prediction of the system load over an interval ranging from one hour to one week. The paper reviews the important role of STLF in the on-line scheduling and security functions of an energy management system (EMS). It then discusses the nature of the load and the different factors influencing its behavior. A detailed classification of the types of load modeling and forecasting techniques is presented. Whenever appropriate, the classification is accompanied by recommendations and by references to the literature which support or expand the discussion. The paper also presents a lengthy discussion of practical aspects for the development and usage of STLF models and packages. The annotated bibliography offers a representative selection of the principal publications in the STLF area.
934 citations
TL;DR: In this paper, an artificial neural network (ANN) method is applied to forecast the short-term load for a large power system, where the load has two distinct patterns: weekday and weekend-day patterns.
Abstract: An artificial neural network (ANN) method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of an ANN for short-term load forecasting were tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers was tested with various combinations of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives a good load forecast. >
546 citations