Fuzzy logic for short term load forecasting

Ranaweera, D.K.; Hubele, Norma Faris; Karady, G.G.

doi:10.1016/0142-0615(95)00060-7

Cited by 82 publications

(40 citation statements)

References 10 publications

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“…Many researchers have been adopting or developing various techniques for STLF to tackle load forecasting problems. A lot of these techniques can be roughly categorized into two groups: statistical approaches, such as Regression Analysis [4] and Time Series Analysis [5], and Artificial Intelligence (AI) based approaches, such as Artificial Neural Network (ANN) [6], Fuzzy Logic (FL) [7], and Support Vector Machine (SVM) [8]. Various combinations of these techniques have also been studied and applied to STLF problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While the early expert systems required a lot of input from operators, researchers started to design automatic fuzzy inference systems [15,16]. An investigation of fuzzy logic model for STLF was presented in [7], where the fuzzy rules were obtained from the historical data using a learning algorithm. The model was used to forecast daily peak load and daily energy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most of these efforts were embedded coherently into the development of the techniques. For instance, temperature and relative humidity were considered in (Ranaweera and Hubele, 1996) while the effect of humidity and wind speed were considered through a linear transformation of temperature in the improved version . In general, the electric load is mainly driven by nature and human activities.…”

Section: Introductionmentioning

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%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Many papers have been published that explore Wavelet decomposition. Most of these papers have featured an approach that uses artificial neural networks [4][5][6][7]. This approach showed relatively high accuracy compared to conventional forecasting methods [7].…”

Section: Introductionmentioning

confidence: 99%

“…They also flexible and robust for nonlinear problem. So, during recent decade, these methods are focused to solve nonlinear relationship between meteorological data and electric load data [4][5][6][7]. There are several hybrid method to complement the single algorithm model.…”

Section: Introductionmentioning

confidence: 99%

Short-term Electric Load Forecasting Based on Wavelet Transform and GMDH

Koo¹,

Lee²,

Park³

2015

Journal of Electrical Engineering and Technology

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-The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

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Reconstructing clusters for preconditioned short‐term load forecasting

Itagaki

Mori

2007

Electrical Engineering Japan

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SUMMARYThis paper presents a new preconditioned method for short-term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short-term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen-based neural network. That enhances a short-term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one-step ahead daily maximum load forecasting.

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Fuzzy logic for short term load forecasting

Cited by 82 publications

References 10 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

Short-term Electric Load Forecasting Based on Wavelet Transform and GMDH

Reconstructing clusters for preconditioned short‐term load forecasting

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