A Neural Network Model to Forecast Urban Electricity Consumptions from Weather Data (original) (raw)
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Forecasting daily urban electric load profiles using artificial neural networks
Energy conversion and …, 2004
The paper illustrates a combined approach based on unsupervised and supervised neural networks for the electric energy demand forecasting of a suburban area with a prediction time of 24 h. A preventive classification of the historical load data is performed during the unsupervised stage by means of a Ko-honenÕs self organizing map (SOM). The actual forecast is obtained using a two layered feed forward neural network, trained with the back propagation with momentum learning algorithm. In order to investigate the influence of climate variability on the electricity consumption, the neural network is trained using weather data (temperature, relative humidity, global solar radiation) along with historical load data available for a part of the electric grid of the town of Palermo (Italy) from 2001 to 2003. The model validation is performed by comparing model predictions with load data that were not used for the networkÕs training. The results obtained bear out the suitability of the adopted methodology for the short term load forecasting (STLF) problem also at so small a spatial scale as the suburban one.
Neural network based approach for short-term load forecasting
2009 IEEE/PES Power Systems Conference and Exposition, 2009
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.
Accurate prediction of electricity demand can bring extensive benefits to any country as the forecasted values help the relevant authorities to take decisions regarding electricity generation, transmission and distribution appropriately. The literature reveals that, when compared to conventional time series techniques, the improved artificial intelligent approaches provide better prediction accuracies. However, the accuracy of predictions using intelligent approaches like neural networks are strongly influenced by the correct selection of inputs and the number of neuro-forecasters used for prediction. suggested that a cluster analysis could be performed to group similar day types, which contribute towards selecting a better set of neuro-forecasters in neural networks. The cluster analysis was based on the daily total electricity demands as their target was to predict the daily total demands using neural networks. However, predicting half-hourly demand seems more appropriate due to the considerable changes of electricity demand observed during a particular day. As such clusters are identified considering half-hourly data within the daily load distribution curves. Thus, this paper is an improvement to , which illustrates how the half hourly demand distribution within a day, is incorporated when selecting the inputs for the neuro-forecasters.
2015
This paper uses Artificial Neural Networks (ANN) for Short Term Load Forecasting (STLF) for a residential area in Yanbu Industrial City (YIC), an industrial city in the western coast of the Kingdom of Saudi Arabia (KSA). Three years data were collected for a residential substation in this city. In recent years, load forecasting raised large interest in power area in KSA. This is due to the increasing rise in number of population, expansion in residential construction, economic growth rate and the rapid developments in the Kingdom. STLF is an important study in the area of system operation and planning. The daily load behavior is affected by many factors such as social, religious, official occasions and environmental conditions. In this paper, two ANN models are proposed which are next hour and next day load forecasting. For next day load forecasting, the load is forecasted using ANN model and by iterative using of next hour model. The obtained results for ANN next hour model yield accurate results. For next day load forecasting, the two models yield satisfactory results. Comparative study is conducted to prove the effectiveness of the models proposed. The results obtained in this work are compared with other published work using different method applied to the same data.
European Transactions on Electrical Power, 2007
ABSTRACT The paper illustrates two different Artificial Neural Networks (ANN) architectures for electric Short-Term Load Forecasting (STLF). Two multi-layer perceptron ANN using the back-propagation learning algorithm have been implemented which provide different, although complementary, forecasting approaches (static and dynamic). In order to test the potentialities of the architectures implemented, the ANN have been applied to the Short-Term Forecasting of Italian hourly electric load. The importance of this load (peak demands up to about 38 000 MW) requires tools for STLF which must be as more accurate and precise as possible. This fact has imposed the adoption of some algorithmic enhancements to the basic back-propagation algorithm formulation. Since an adequate formulation of the influence exerted on hourly electric load by the main meteorological and climatic factors is not known at present, the data set used for ANN training phase has concerned only historical series of electric hourly demand. The paper illustrates the two ANN architectures as well as the computational platforms used for implementation. Finally, some results obtained from the application of the two ANN to the short-term forecasting of Italian electric load relevant to three different weeks of the year 1993 are comparatively reported.
Short-Term Electric Load Forecasting Using Neural Networks
Computer as a Tool, …
Short term electric load forecasting with a neural network based on fuzzy rules is presented. In this network, fuzzy membership functions are represented using combinations of two sigmoid functions. A new scheme for augmenting the rule base is proposed. The network employs outdoor temperature forecast as one of the input quantities. The influence of imprecision in this quantity is investigated. The model is shown to be capable of also making reasonable forecasts in exceptional weekdays. Forecasting simulations were made with three different time series of electric load. In addition, the neurofuzzy method was tested at two electricity works, where it was used to produce forecasts with 1-24 hour lead times. The results of these one month real world tests are represented. Comparative forecasts were also made with the conventional Holt-Winters exponential smoothing method. The main result of the study is that the neuro-fuzzy method requires stationarity fi~om the time series with respect to training data in order to give clearly better forecasts than the Holt-Winters method.
A hierarchical neural model in short-term load forecasting
Applied Soft Computing, 2004
This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) nets-one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24 h. The paper presents the results, the conclusions, and points out some directions for future work.
A Practical Approach for Electricity Load Forecasting
2005
This paper is a continuation of our daily energy peak load forecasting approach using our modified network which is part of the recurrent networks family and is called feed forward and feed back multi context artificial neural network (FFFB-MCANN). The inputs to the network were exogenous variables such as the previous and current change in the weather components, the previous and current status of the day and endogenous variables such as the past change in the loads. Endogenous variable such as the current change in the loads were used on the network output. Experiment shows that using endogenous and exogenous variables as inputs to the FFFB-MCANN rather than either exogenous or endogenous variables as inputs to the same network produces better results. Experiments show that using the change in variables such as weather components and the change in the past load as inputs to the FFFB-MCANN rather than the absolute values for the weather components and past load as inputs to the same network has a dramatic impact and produce better accuracy.
An efficient approach for short term load forecasting using artificial neural networks
International Journal of …, 2006
In previous work, we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three types of variables were used as inputs to the neural network: (a) hour and day indicators, (b) weather related inputs and (c) historical loads. In general, for forecasting with a lead time of up to a few days ahead, load history (for the last few days) is not available, and therefore, estimated values of this load are used instead. However, a small error in these estimated values may grow up dramatically and lead to a serious problem in load forecasting since this error is fed back as an input to the forecasting procedure. In this paper, we demonstrate ANN capabilities in load forecasting without the use of load history as an input. In addition, only temperature (from weather variables) is used, in this application, where results show that other variables like sky condition (cloud cover) and wind velocity have no serious effect and may not be considered in the load forecasting procedure.
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/short-term-load-forecasting-using-artificial-neural-networks-for-a-residential-area-in-an-industrial-city https://www.ijert.org/research/short-term-load-forecasting-using-artificial-neural-networks-for-a-residential-area-in-an-industrial-city-IJERTV4IS010454.pdf This paper uses Artificial Neural Networks (ANN) for Short Term Load Forecasting (STLF) for a residential area in Yanbu Industrial City (YIC), an industrial city in the western coast of the Kingdom of Saudi Arabia (KSA). Three years data were collected for a residential substation in this city. In recent years, load forecasting raised large interest in power area in KSA. This is due to the increasing rise in number of population, expansion in residential construction, economic growth rate and the rapid developments in the Kingdom. STLF is an important study in the area of system operation and planning. The daily load behavior is affected by many factors such as social, religious, official occasions and environmental conditions. In this paper, two ANN models are proposed which are next hour and next day load forecasting. For next day load forecasting, the load is forecasted using ANN model and by iterative using of next hour model. The obtained results for ANN next hour model yield accurate results. For next day load forecasting, the two models yield satisfactory results. Comparative study is conducted to prove the effectiveness of the models proposed. The results obtained in this work are compared with other published work using different method applied to the same data.