Comparative Study of Short-Term Electric Load Forecasting

Koo, Bon-Gil; Lee, Sangwook; Kim, W.; Park, June Ho

doi:10.1109/isms.2014.85

Cited by 16 publications

(11 citation statements)

References 10 publications

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“…Xu et al applied GMDH networks in comparison with ARIMA for short-term load forecasting of New South Wales in Australia [32]. Koo et al presented a comparative study that performed ANN, simple exponential smoothing (SES), and GMDH networks for forecasting Korean electric load data on an hourly basis [33], and another study that wavelet transform was firstly applied for decomposition before the implementation of Holt-Winters method, ANN, and GMDH networks for one day ahead forecasting of hourly electric loads [34]. Jacob et al employed GMDH networks and linear regression (LR) for forecasting short-term electrical energy consumption of a university campus in Nigeria [35].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

et al. 2020

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Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

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Section: Related Workmentioning

confidence: 99%

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

et al. 2020

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“…The authors (Koo, Lee, Kim, & Park, 2014) in their study performed short-term electric load forecasting using three methods and compared the results. They used two factors to eliminate error from a calendarbased classification before making a forecasting model.…”

Section: Review Of Electric Power Load Forecastingmentioning

confidence: 99%

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Al-Hamad¹,

Qamber

2019

Arab Journal of Basic and Applied Sciences

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It is important to develop a suitable model to calculate electricity demand forecasting requested by decision makers. The present study deals with the electrical long-term peak load demand forecasting using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods. The MLR model is formulated as a function of population and Gross Domestic Product (GDP) for the Gulf Cooperation Council (GCC) region. The Neuro-Fuzzy is thereafter trained using previous sets of data. This training gives a future annual electricity load prediction. The results obtained from the developed models have an acceptable level of mean errors. In general, the GCC region has high-energy consumption influenced by a number of factors, such as population and GDP. The annual variation of both population and GDP growth scenarios is based on development in the country. The obtained results will encourage the GCC through the energy field development and setting the future plans for it. The novelty of the present study is to avoid an increase in generation capacity in mid-term and long-term plans, which will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. The developed models will help the economic development of the GCC countries. It also helps with finding the optimum time for electrical energy trading. The obtained results for the GCC illustrate the average percentage error calculated which was found to be close to 2% and 0.53% in multilinear regression and Neuro-Fuzzy, respectively. These results reduce capital investment, limiting the equipment installed and the expected load needed for better load distribution in the region. In conclusion, the Neuro-Fuzzy is the most accurate technique compared with MLR to estimate future electricity demand and, at the same time, it can be used in power system planning and development.

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“…Its basic equation is called Kolmogrov-Gabor polynomial and it is expressed as shown in equation (1) below [11].…”

Section: Literaturementioning

confidence: 99%

“…For example, neuron can be represented with a quadratic polynomial function as shown in equation (2), (2) In principle, constructed network is the composition of neurons with the mapping function f (x i, x j ). The fixed number of neurons is selected at each layer and the output of these neurons is used on the next layer [11].…”

Section: Literaturementioning

confidence: 99%

Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network

Jacob¹,

Usman

Saka³

et al. 2015

JEEE

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Electric load forecasting plays an important role in the planning and operation of the power system for high productivity in any institution of learning. A short-term electrical energy forecast for Gidan Kwano campus, Federal University of Technology Minna, Nigeria was carried out using GMDH-type neural network and the result was compared to that of regression analysis. GMDH-type neural network was used to train and test weekly energy consumed in the campus from September 2010 to December 2014. The neural network was trained using quadratic neural function. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as performance indices to test the accuracy of the forecast. The neural network model gave a root mean square error (RMSE) of 0.1189, a mean absolute percentage error (MAPE) of 0.0922 and a correlation (R) value of 0.8995 while the regression analysis method gave a standard error of 10968.1 and a correlation (R) value of 0.1137. Results obtained show the efficacy of the GMDH-type neural network model in forecasting over the regression analysis method.

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Comparative Study of Short-Term Electric Load Forecasting

Cited by 16 publications

References 10 publications

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network

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