Experience with Weather Sensitive Load Models for Short and Long-Term Forecasting

Corpening, S. L.; Reppen, N.D.; Ringlee, R. J.

doi:10.1109/tpas.1973.293576

Cited by 31 publications

(6 citation statements)

References 3 publications

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“…The method has low input requirements for the load forecasting model, which only considers the time series input of historical data and does not consider other multi-faceted influencing factors that affect the load. The literature [18] provides a load peak model that takes external factors into account such as weather and humidity. Based on the Box-Jenkins method, Hagan et al [19] proposed an autoregressive moving average model (ARMA) model prediction method, and Juberias et al [20] established the autoregressive integral moving average model (ARIMA) model to achieve load forecasting.…”

Section: Literature Studymentioning

confidence: 99%

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

et al. 2019

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Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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Section: Literature Studymentioning

confidence: 99%

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

et al. 2019

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“…On the basis of information in the literature (Corpening et al, 1973) and several inquiries, we estimate that the use of such weather sensitive electricity load models can result in a reduction in the standard short-term forecast error for some utilities of 0.5-3.0%, depending on the present accuracy of a utility's short-term load model and on the characteristics of its load (e.g., whether it has a large amount of industrial base load). If a 0.5% improvement in forecast error due to improved meteorological services were directly translatable into a 0.5% reduction in excess electricity generation, this would result in conservation of 0.1% of the national energy consumption.…”

Section: ) Potential Energy Savingsmentioning

confidence: 99%

Potential Use of Applied Meteorology in Energy Conservation Programs

Murphy¹,

Schulman²,

Mahoney³

1977

Bull. Amer. Meteor. Soc.

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This study analyzes nine areas of the national economy in terms of their potential for energy conservation through the use of applied meteorological services. In addition to a quantitative estimate for the potential energy savings, each area is also discussed in terms of: 1) the way in which applied meteorological services are currently provided, 2) the non-meteorological impediments to supplying improved meteorological services, and 3) the required research and implementation program to provide these improved meteorological services. It is concluded that applied meteorology could represent a significant increase in the nation's efficient use of the available energy supplies. Savings of up to several percent of the nation's annual energy consumption could be realized. This would represent an economic savings of as much as $5-$6 billion annually. In addition, there are opportunities related to the use of supplementary control systems at fossil-fired power plants that would allow cheaper, more abundant fuels to be used under appropriate meteorological conditions. Candidates for these control systems could represent up to 8.5% of the nation's energy consumption and effect the use of fuels equivalent to over a billion barrels of oil. None of the above estimates account for the implementation costs of required programs. Development of these costs would involve judgments concerning future technological innovations and utilization patterns, which are beyond the scope of the present inquiry. However, in view of the substantial economic and energy savings available, it is apparent that these costs are worthy of investigation.

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“…Depending on the period of the forecast done, load forecasting can be classified into three different types [8].…”

Section: Introductionmentioning

confidence: 99%

Short Term Electric Load Forecasting of 132/33KV Maiduguri Transmission Substation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Harrison¹,

Dan'isa²,

A.³

et al. 2014

IJCA

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This article provides a way of accurately predicting one-hourahead load of a utility company located in the North Eastern region of Nigeria based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The inputs to the ANFIS are the next-hour temperature, next-hour humidity, day of the week, hour of the day, and the current-hour load. The output is the next-hour load of the entire system. All the data used span the period 2009 to 2012 (4 years). These parameters are non-linear, stochastic (random) and uncertain in nature. Adaptive Neurofuzzy based Inference System (ANFIS), an integrated system, comprising of fuzzy logic and Neural Network was used to model the next hour load, because it can address and solve problems related to non-linearity, randomness and uncertainty of data. 75% of the data was used for training and 25% for checking. From the analysis carried out on the ANFIS-based model; Mean absolute percentage error (MAPE) for a typical Monday, Wednesday and Friday was found to be 12.61%, 12.76% and 12.12%. The Mean absolute error (MAPE) on the entire test data was 24.76%. The analysis shows satisfactory level of accuracy with regards to the ANFIS-based model developed in forecasting the next hour load especially with a correlation (r) value of 84.64%. General TermsLoad Forecasting.

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Experience with Weather Sensitive Load Models for Short and Long-Term Forecasting

Cited by 31 publications

References 3 publications

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Potential Use of Applied Meteorology in Energy Conservation Programs

Short Term Electric Load Forecasting of 132/33KV Maiduguri Transmission Substation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

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