Short-term Load Forecasting Using Improved Similar Days Method

Mu, Qingqing; Wu, Yirong; Pan, Xiaoqiang; Huang, Liangyi; Xian, Li

doi:10.1109/appeec.2010.5448655

Cited by 41 publications

(22 citation statements)

References 2 publications

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“…The SD method is based on the selection of historical days that have similar features to the forecasted days [5][6][7][8][9]. Mandal et al [7] selected SDs based on the calculation of the Euclidean norm of factors between historical and forecasted days.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

2017

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Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

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Section: Introductionmentioning

confidence: 99%

“…Chen et al [8] required SDs to have the same weekday index and similar weather to the forecasted days. Mu [9] applied a weighted average model for the historical day to determine the influence of most SDs on the forecasted day. However, using this method solely cannot sufficiently obtain high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

2017

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“…Mu et al studied 7-day-ahead load forecasting for Hainan, China using the similar day method [12]. The input data were chosen as load data, weather conditions, temperature data, day type, sociological conditions, and day difference in their study.…”

Section: Literature Overviewmentioning

confidence: 99%

“…These factors can be defined as consumption region and independent variables like day of the week, sociological, meteorological, demographic, and economic conditions. Demographic and economic conditions are generally used in long-term load forecasting studies [3], and temperature data are mostly used as an independent variable in STLF studies [1,2,[4][5][6][7][8][9][10][11][12][13][14]. In addition to temperature data, other meteorological variables like wind, humidity, and precipitation are used in studies [2,7,9,11,15].…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting without meteorological data using AI-based structures

Esener¹,

Yüksel²,

Kurban³

2015

Turk J Elec Eng & Comp Sci

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STLF is used in making decisions about economical power generation capacity, fuel purchasing, safety assessment, and power system planning in order to have economical power conditions. In this study, Turkey's 24-hourahead load forecasting without meteorological data is studied. ANN, wavelet transform and ANN, wavelet transform and RBF NN, and EMD and RBF NN structures are used in STLF procedures. Local holidays' historical load data are changed into data with normal day characteristics, and the estimation results of these days are not included in error computation. To obtain more accurate results, a regulation on forecasted loads is proposed. Regulated and unregulated forecasting error percentages are computed as daily average MAPE and maximum daily MAPE, and compared between the proposed structures. A simulation is performed for the years 2009-2010 via the user interface created using MATLAB GUI.

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“…Short-term forecasting, it is used to supply necessary information for the system management of day-to-day operations and unit commitment [1] [5]. [7]. 2) Regression based Approach: Linear regression is a technique which examines the dependent variable to specified independent.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Different Types of Load Forecasting Methods and the Factors Affecting the Load Forecasting

Gupta¹

2017

IJRASET

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load forecasting is vitally important for the electric industry in the deregulated economy. Load forecasting is about estimating future consumptions based on various data and information available and as per consumer behavior. Load forecasting mean forecasting average load in kw or total load in kwh for blocks of hourly, daily, weekly, monthly or yearly. There are different tools and techniques for load forecasting. One of the primary tasks of an electric utility is to accurately predict load demand requirements at all times, especially for short term. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. Hence knowing the load behavior in advance is very important in planning, analysis and operation of power systems to maintain an uninterrupted, reliable, secure and economic energy providing. Keywords: electric load forecasting, short time load forecasting (stlf), long term load forecasting (ltlf) , medium term load forecasting (mtlf) I.INTRODUCTION Load forecasting is extremely important in electric energy generation, transmission, distribution and markets. Load forecast has been a central and an integral process in the planning and operation of electric utilities. The Purpose of load forecasting is proper planning and operation of a power utility requires an adequate model for electric power load forecasting [1]. Load forecasting plays a key role in helping an electric utility to make important decisions on power market, load switching, voltage control, network reconfiguration, and infrastructure development. Here we have discussed three type of load forecasting which are:-Long-term electric load forecasting used to supply electric utility company management with prediction of future needs for expansion, equipment purchases or staff hiring. This is longer than a year. Medium-term forecasting, used for the purpose of scheduling fuel supplies and unit maintenance. This is usually from a week to a year. Short-term forecasting, it is used to supply necessary information for the system management of day-to-day operations and unit commitment [1] [5]. II. LOAD FORECASTING MODEL DEVELOPMENT

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Short-term Load Forecasting Using Improved Similar Days Method

Cited by 41 publications

References 2 publications

Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

Short-term load forecasting without meteorological data using AI-based structures

An Overview of Different Types of Load Forecasting Methods and the Factors Affecting the Load Forecasting

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