Short-Term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms

Hinojosa, Víctor; Hoese, A.

doi:10.1109/tpwrs.2009.2036821

Cited by 113 publications

(76 citation statements)

References 35 publications

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“…We trained the ARIMA with all the existing previous data, starting from the first 12 days of the same type, and forecasted the rest of the values until the end of the year. The AR didn't need training and the NN, contrary to what the literature claims [1], [2],needed surprisingly exiguous training data: only the last 3 same day-type and hour values. We believe that the reason for this phenomenon is that we skipped non-helpful training data by feeding the NN only with same day-type and hour values.…”

Section: Experiments and Discussionmentioning

confidence: 84%

“…Their output is a linear o non-linear function of the inputs and, therefore, they have been widely used for predicting non-linear data (as in STLF [1], [2], [20], [21], [25], [26]). After many tests, we obtained the best results with a NN design including the temperature-related variables, the value of the previous hour (independently of the day type), and the value of the same hour in the previous same-type day.…”

Section: A Modelsmentioning

confidence: 99%

“…RELATED WORK As already mentioned, there exists a very large literature on short-term load forecasting (see [1], [2], [4] for a comprehensive survey on STLF) but, comparatively, little on the same topic applied to buildings. In both scenarios, research presents two main branches.…”

Section: Introductionmentioning

confidence: 99%

“…Though being most promising, NN and SVM must deal with a number of problems. First, either require much more historical data than any of the statistical methods [2]. This data set may also pose a problem to NNs since they fail when it presents random correlations among the inputs and the output because conventional NNs will not set the coefficients for those junk inputs to zero.…”

Section: Introductionmentioning

confidence: 99%

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Short-term load forecasting in air-conditioned non-residential Buildings

Penya

Borges

Agote

et al. 2011

2011 IEEE International Symposium on Industrial Electronics

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Abstract-Short-term load forecasting (STLF) has become an essential tool in the electricity sector. It has been classically object of vast research since energy load prediction is known to be nonlinear. In a previous work, we focused on non-residential building STLF, an special case of STLF where weather has negligible influence on the load. Now we tackle more modern buildings in which the temperature does alter its energy consumption. This is, we address here fully-HVAC (Heating, Ventilating, and Air Conditioning) ones. Still, in this problem domain, the forecasting method selected must be simple, without tedious trial-and-error configuring or parametrising procedures, work with scarce (or any) training data and be able to predict an evolving demand curve. Following our preceding research, we have avoided the inherent non-linearity by using the work day schedule as day-type classifier. We have evaluated the most popular STLF systems in the literature, namely ARIMA (autoregressive integrated moving average) time series and Neural networks (NN), together with an Autoregressive Model (AR) time series and a Bayesian network (BN), concluding that the autoregressive time series outperforms its counterparts and suffices to fulfil the addressed requirements, even in a 6 day-ahead horizon.

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Section: Experiments and Discussionmentioning

confidence: 84%

Section: A Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-term load forecasting in air-conditioned non-residential Buildings

Penya

Borges

Agote

et al. 2011

2011 IEEE International Symposium on Industrial Electronics

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“…Thus, to stabilize power supply and demand, the Ministry of Trade, Industry & Energy (MOTIE) has announced a basic plan for power supply and demand called the powerdemand forecast and management plan [3]. It is essential to be able to predict the power supply and demand when establishing a power-demand management plan [4][5][6][7]. To accurately predict the power supply and demand, it is necessary to predict the number of charging EVs, connected to the distribution system.…”

Section: Introductionmentioning

confidence: 99%

New Prediction of the Number of Charging Electric Vehicles Using Transformation Matrix and Monte-Carlo Method

Go¹,

Ryu²,

Kim³

et al. 2017

Journal of Electrical Engineering and Technology

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-An Electric Vehicle (EV) is operated with the electric energy of a battery in place of conventional fossil fuels. Thus, a suitable charging infrastructure must be provided to expand the use of electric vehicles. Because the battery of an EV must be charged to operate the EV, expanding the number of EVs will have a significant influence on the power supply and demand. Therefore, to maintain the balance of power supply and demand, it is important to be able to predict the numbers of charging EVs and monitor the events that occur in the distribution system. In this paper, we predict the hourly charging rate of electric vehicles using transformation matrix, which can describe all behaviors such as resting, charging, and driving of the EVs. Simulation with transformation matrix in a specific region provides statistical results using the Monte-Carlo Method.

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