Clustering based day-ahead and hour-ahead bus load forecasting models

Panapakidis, Ioannis P.

doi:10.1016/j.ijepes.2016.01.035

Cited by 68 publications

(29 citation statements)

References 22 publications

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“…Panapakidis et al [23] carried out STLF in small-size loads (i.e., the buses of transmission and distribution systems). They conducted day-ahead and hour-ahead load prediction, and proposed models based on ANNs and SOM.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

“…Typically, these seasonal and weekly effects should be taken into account when selecting features and modeling. For instance, the season and day of the week are regarded as categorical variables; hence, they are usually transformed into integers [4] or one-hot encoded vectors [23]. However, in our work, we assume that SOM automatically filters out these features.…”

Section: Exploratory Data Analysis (Eda) For Load Datamentioning

confidence: 99%

“…The clustering technique, self-organizing map (SOM), has also been successfully employed prior to the forecasting task to improve the performance [11,[22][23][24]. In the first stage, SOM is used to cluster the training set into separate subsets in an unsupervised scheme, such that the subsets have similar patterns and characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Lee

Kim

2019

Applied Sciences

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Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load forecasting. Self-organizing map (SOM) has been successfully utilized to cluster a dataset into subsets containing similar data points. These subsets are then used to train the forecasting models to improve forecasting accuracy. However, some buildings may have insufficient data since newly installed monitoring devices such as AMI have no choice but to collect a limited amount of data. Using a clustering technique on small datasets could lead to overfitting when using forecasting models following an SOM network to be trained with clusters. This results in a relatively high generalization error. In this study, we propose to address this problem by employing the stacking ensemble learning method (SELM) that is well-known for its generalization ability. An experimental study was conducted using the electricity consumption data of an actual institutional building and meteorological data. Our proposed model outperformed other baseline models, which means it successfully mitigates the effect of overfitting.

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Section: Self-organizing Map (Som)mentioning

confidence: 99%

Section: Exploratory Data Analysis (Eda) For Load Datamentioning

confidence: 99%

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Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Lee

Kim

2019

Applied Sciences

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“…The importance of electricity demand forecasting has been pointed out by many studies of the electricity market, Such forecasting are categorized as very short-term, short-term, mid-term, and long-term classes. The proceedings [2][3][4][5] pointed out the electricity forecasting methods for very short-term intervals (shorter than an hour). The proceedings [6][7][8][9][10] introduced the forecasting methods for mid-term intervals (ranging from one month to one year), and the proceedings [11][12][13][14][15][16] pointed out the long-term intervals (longer than one year).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Salami

Sobhani

Ghazizadeh

2018

Data

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The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

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“…The used methods can be divided into three as statistical methods, artificial intelligence methods and hybrid methods. The most commonly used techniques are based on Regression models [4], Times series models [5], [6], ARIMA models [7], Artificial Neural Network models [8], [9], Fuzzy models [10], [11], Support vector machine models [12], Particle swarm optimization models [13], Genetic algorithm models [14], [15], wavelet transform [16], ANFIS [11]. This paper focuses on a hybrid method based on the combination of artificial bee colony (ABC) and artificial neural network (ANN) for short term load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting based on ABC and ANN for Smart Grids

Çevik

Harmanci²

2016

ijisae

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Short term load forecasting is a subject about estimating future electricity consumption for a time interval from one hour to one week and it has a vital importance for the operation of a power system and smart grids. This process is mandatory for distribution companies and big electricity consumers, especially in liberalized energy markets. Electricity generation plans are made according to the amount of electricity consumption forecasts. If the forecast is overestimated, it leads to the start-up of too many units supplying an unnecessary level of reserve, therefore the production cost is increased. On the contrary if the forecast is underestimated, it may result in a risky operation and consequently power outages can occur at the power system. In this study, a hybrid method based on the combination of Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) is developed for short term load forecasting. ABC algorithm is used in ANN learning process and it optimizes the neuron connections weights of ANN. Historical load, temperature difference and season are selected as model inputs. While three years hourly data is selected as training data, one year hourly data is selected as testing data. The results show that the application of this hybrid system produce forecast values close to the actual values.

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Clustering based day-ahead and hour-ahead bus load forecasting models

Cited by 68 publications

References 22 publications

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Short-term Load Forecasting based on ABC and ANN for Smart Grids

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