Estimating temperature profiles for short-term load forecasting: neural networks compared to linear models

Hippert, Henrique S.; Pedreira, Carlos Eduardo

doi:10.1049/ip-gtd:20040491

Cited by 36 publications

(16 citation statements)

References 22 publications

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“…Weather forecast, as an input to the extrapolating process, is also very important to the accuracy of STLF. Consequently, another branch of research work is focusing on developing, improving or incorporating the weather forecast [11].…”

Section: Literature Reviewmentioning

confidence: 99%

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Short Term Load Forecasting of 132/33kv Kano Transmission Substation using Fuzzy Logic Model

Abdulazeez¹,

Ibrahim²

2017

IJCA

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This paper provides a short term load forecasting methodology using fuzzy logic for accurately predicting the load requirement of a utility company located in the Northwest region of Nigeria. Fuzzy logic approach is implemented on the daily average temperature data and historical load data of 132/33KV Kano Transmission Substation obtained from Power Holding Company of Nigeria (PHCN) for a period of one year for forecasting the load. The methodology employed uses fuzzy reasoning decision rules that capture the nonlinear relationships between inputs and outputs. Fuzzy rule base used for the forecast were prepared using mamdani implication. Simulink in MATLAB environment is used in this work. The results for the forecasted load are obtained from fuzzy logic model using triangular membership function. The forecast result deviation from the actual values is presented in the form of Mean Absolute Percentage Error (MAPE). From the analysis carried out, the simulated results of the developed model were found to be very close to the one obtained from the Power Utility with Mean Absolute Percentage Error (MAPE) value of 4.65% for Monday 3 rd March 2014 and a MAPE value of 3.08% for Monday 9 th March 2014 which is an indication that the results obtained using the fuzzy logic approach are accurate enough for electricity load forecast.

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Section: Literature Reviewmentioning

confidence: 99%

“…One of the challenges behind this task is that power demand in a given place does vary with growth in population and economic activities [1]. Outcomes obtained from load forecasting progressions are used in different endeavors in power sector such as planning, system expansion, maintenance and operational schedule.…”

Section: Introductionmentioning

confidence: 99%

Short Term Load Forecasting of 132/33kv Kano Transmission Substation using Fuzzy Logic Model

Abdulazeez¹,

Ibrahim²

2017

IJCA

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“…In order to get more accurate forecast, most of existing studies focus on improving load forecast methods [7][8][9][10][11][12][13][14][15][16][17], or transforming annual meteorological parameter library to account for the climate change [5,6,18]. http Although such approaches can improve the accuracy of load forecast, they still have some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…http Although such approaches can improve the accuracy of load forecast, they still have some drawbacks. Firstly, popular load forecast methods, such as artificial neural network (ANN), time series analysis method and grey theory, all these methods need to base on abundant daily or hourly historical load records to train or modify their models [7,9,10,[12][13][14][15]17]. Secondly, regarding load simulation software, weather data used to simulate the cooling load for any given day could not resemble the actual conditions of that day as the existing weather data is transformed to account for future changes in the macro climate [5,6,18].…”

Section: Introductionmentioning

confidence: 99%

A research on a following day load simulation method based on weather forecast parameters

Wang

Cheng

2015

Energy Conversion and Management

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“…ANN modelling is well established as a tool for electric load forecasting, both as a stand-alone tool (da Silva and Moulin, 2000;Hippert et al, 2001;Mori and Yuihara, 2001;Marin et al, 2002;Taylor and Buizza, 2002;Beccali et al, 2004;Hippert and Pedreira, 2004;Musilek et al, 2006;Hinojosa and Hoese, 2010) and as a hybrid (Khotanzad et al, 1998;Srinivasan et al, 1999;Huang and Yang, 2001;Ling et al, 2003;Chen et al, 2004;Fan and Chen, 2006;Liao and Tsao, 2006;Amjady, 2007;Yun et al, 2008;Bashir and El-Hawary, 2009). While the ANN node-connection diagram is relatively simple (Hsieh, 2009), users might forget that a very large number of free parameters must be determined for ANNs.…”

Section: Introductionmentioning

confidence: 99%

Electric Load Forecasting for Western Canada: A Comparison of Two Non-Linear Methods

Bakhshaii

Stull

2012

Atmosphere-Ocean

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Seven years of hourly temperature and electric load data for British Columbia in western Canada were used to compare two statistical methods, artificial neural networks (ANN) and gene expression programming (GEP), to produce hour-ahead load forecasts. Two linear control methods (persistence and multiple linear regression) were used for verification purposes. A two-stage (predictor-corrector) approach was used. The first stage used a single regression model that applied weather and calendar data for the previous hour to predict load for any hour of the day, and the second stage applied different corrections every hour, based on a high correlation of today's load with yesterday's load for any given hour. By excluding the day-before variables, the two-stage method reduced the total number of variables in the first-stage regression and gave better results. The first five years of data were used for training (regression finding) and validation (comparative testing of candidate functions to reduce overfitting) and the last two for verification (scoring with independent data).It was found that both non-linear methods worked better than the linear methods for the first stage. All methods worked well for the second stage; hence, persistence is recommended for the second stage because it is easiest to do. After both stages, the load error was less than 0.6% of the load magnitude for hour-ahead forecasts. When used iteratively to create forecasts up to 24 hours ahead, errors grew to about 3.5% of the load magnitude for both GEP and ANNs. We also experimented by training the statistical methods with a shorter period (one year) of past data to examine the over-fitting problem. Overall, ANNs were more useful in fitting the curves to robust data, and GEP was superior for short datasets and was less sensitive to the length of the dataset. We also found that a time-nested electric load forecasting model with different lead times kept maximum load errors to 2.1% of the load magnitude out to a horizon of 24 forecasts.RÉSUMÉ [Traduit par la rédaction] Nous avons utilisé sept années de données de température et de charge électrique pour la Colombie-Britannique dans l'ouest du Canada pour comparer deux méthodes statistiques, les réseaux neuronaux artificiels (ANN) et la programmation génétique (GEP), pour produire des prévisions de charge une heure à l'avance. Nous nous sommes servis de deux méthodes de vérification linéaires (persistance et régression linéaire multiple) à des fins de vérification. Nous avons utilisé une approche en deux étapes (prédicteur-correcteur). Dans la première étape, un modèle de régression simple appliquait des données météorologiques et de calendrier pour l'heure précédente pour prévoir la charge à n'importe quelle heure de la journée et la deuxième étape appliquait à chaque heure différentes corrections basées sur une forte corrélation de la charge d'aujourd'hui avec la charge d'hier pour une heure quelconque. En excluant les variables du jour précédent, la méthode à deux étapes a réduit le nombr...

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Estimating temperature profiles for short-term load forecasting: neural networks compared to linear models

Cited by 36 publications

References 22 publications

Short Term Load Forecasting of 132/33kv Kano Transmission Substation using Fuzzy Logic Model

Short Term Load Forecasting of 132/33kv Kano Transmission Substation using Fuzzy Logic Model

A research on a following day load simulation method based on weather forecast parameters

Electric Load Forecasting for Western Canada: A Comparison of Two Non-Linear Methods

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