An Optimized Adaptive Neural Network for Annual Midterm Energy Forecasting

Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…Ref. [67] considered the horizon of MTLF, as few years ahead with a forecast step of one year, and it is more LTLF than MTLF. Refs.…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

120

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“…Typically, the output of MLTF is the daily peak and average load [19,20]. MLTF has a strong relationship with STLF.…”

Section: Long Term Load Forecastingmentioning

confidence: 99%

Short Term Load Forecasting Using a Neural Network Based Time Series Approach

Dwijayanti

Hagan

2013

2013 1st International Conference on Artificial Intelligence, Modelling and Simulation

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“…Up to now different CV techniques based on the validation set have been proposed for price and especially load forecasting. Some researchers simply considered the training set also as the validation set [26], which is used when the training set is small and cannot be divided into two subsets. Some others considered the nearest time period as the validation set, which is useful when the signal has a strong correlation with its short-run trend [27].…”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 99%

Bayesian neural network model to predict day‐ahead electricity prices

Vahidinasab¹,

Jadid²

2008

Int Trans Elec Energy Syst

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SUMMARYMarket participants in the new competitive electricity markets find price forecasting so valuable in developing their bidding strategies. In this paper, the Bayesian neural network (BNN) approach is proposed for day-ahead prediction of locational marginal prices (LMPs) in the pool based energy markets, in which to select the optimum inputs of the BNN, the correlation coefficient technique is used. LMP has a volatile and time dependent behavior. Hence, prediction of such a complex signal is a challenging task requiring a qualified forecasting tool, which not only fits well to the training data, but also can predict the stochastic behavior of unseen part of the signal. The proposed Bayesian approach to predict day-ahead electricity prices presents noteworthy advantages over the classical neural networks (NN) methods which include the avoidance of network overfitting, an indication of the degree of uncertainty in the predictions, automatic selection of an appropriate scale for network weights and, accordingly, selection of the optimal forecasting model. Examining the proposed forecast strategy on the different periods of the PennsylvaniaNew Jersey-Maryland (PJM) market, it is demonstrated that the proposed method can provide more accurate results than the other price forecasting techniques, such as, ARIMA time series, wavelet-ARIMA, classical NN, and also a similar day method.

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An Optimized Adaptive Neural Network for Annual Midterm Energy Forecasting

Cited by 84 publications

References 14 publications

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Short Term Load Forecasting Using a Neural Network Based Time Series Approach

Bayesian neural network model to predict day‐ahead electricity prices

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