Short-term Electric Load Forecasting Research Articles

Short term electricity load forecasting using a hybrid model

Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models.

Mixed kernel based extreme learning machine for electric load forecasting

Abstract Short term electric load forecasting, as an important tool in the electricity market, plays a critical role in the management of electric systems. Proposing an accuracy and optimization method is not only a challenging task but also an indispensable part of the energy system. More and more accurate forecasting methods are needed by different people in different areas. This paper proposes a novel short-term electric load forecasting method EMD-Mixed-ELM which based on empirical mode decomposition (EMD) and extreme learning machine (ELM). EMD-Mixed-ELM first uses the empirical mode decomposition to decompose the load series for capturing the complicated features of the electric load and de-noising the data. Considering that the performance of extreme learning machine (ELM) is greatly influenced by the choice of kernel, the mixed kernel method is proposed for ELM. The mixed kernel combines the RBF kernel and the UKF kernel. The forecasting results of the EMD-Mixed-ELM are proved to be better than all the other three methods (RBF-ELM, UKF-ELM and Mixed-ELM) and other existing methods (MFES, ESPLSSVM and Combined method). To verify the forecasting ability of the EMD-Mixed-ELM, half-hourly electric load data from the state of New South Wales, Victoria and Queensland in Australia are used in this paper as a case study. The experimental results clearly indicate that for this three datasets, the forecasting accuracy of the proposed method is superior to other methods.

A two-stage short-term load forecasting approach using temperature daily profiles estimation

Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.

Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy.

A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Extreme Learning Machine (ELM) based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM), Wavelet Denoising-Extreme Learning Machine (WKM) and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM), the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm

Short-term electric load forecasting (STLF) has been one of the most active areas of research because of its vital role in planning and operation of power systems. Additionally, intelligent methods are increasingly popular in forecasting model applications. However, the observed data set is often contaminated and nonlinear by as a result of such that it becomes difficult to enhance the accuracy of STLF. Therefore, the novel model (CS-SSA-SVM) for electric load forecasting in this paper was successfully proposed by the combination of SSA (singular spectrum analysis), SVM (support vector machine) and CS (Cuckoo search) algorithms. First, the signal filtering technique (SSA) is applied for data pre-processing and the novel model subsequently models the resultant series with different forecasting strategies using SVM optimized by the CS algorithm. Finally, experiments of electric load forecasting are used as illustrative examples to evaluate the performance of the developed model. The empirical results demonstrated that the proposed model (CS-SSA-SVM) can improve the performance of electric load forecasting considerably in comparison with other methods (SVM, CS-SVM, SSA-SVM, SARIMA and BPNN).

Improving the principles of short-term electric load forecasting of the Irkutsk region

Forecasting of electric load (EL) is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC SO UPS Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region.

Data Selection and Fuzzy-Rules Generation for Short-Term Load Forecasting Using ANFIS

Forecasting accuracy depends on data identification and model parameters. Volume of data and good analysis are the key factors that influence the accuracy of forecasting algorithm. This paper focused on data analysis with aim of determining the actual variables that affect the load consumption. Correlation analysis was used to determine how the load consumption is related to the forecasting variables (model inputs), and hypothesis test to justify the correlation coefficient of each variable. This produced tree different scenarios which ware used to forecast the load within short-term time frame. On the other hand, subtractive clustering and Fuzzy c-means (FCM) algorithms ware compared in fuzzy rules generation using Adaptive Neuro-Fuzzy Inference System (ANFIS) model, for short term electric load forecasting. Forecasting using Hypothesis test data with Subtractive clustering algorithm gave better accuracy compared to the other two approaches. But FCM algorithm is faster in all the three approaches. In conclusion, hypothesis test on the correlation coefficient of the data is a commendable practice for data selection and analysis in short-term load forecasting. Also, subtractive clustering algorithm is good in generating appropriate number of fuzzy rules, and the number depends on the number of input variables. Fuzzy c-means algorithm reduces the number of the rules irrespective of the number of input variables.

A RESEARCH ON SIGNIFICANCE OF KALMAN FILTER-APPROACH AS APPLIED IN ELECTRICAL POWER SYSTEM

Recently, AC distribution systems have experienced high harmonic pollution due to the fact that electrical power system parameters are often mixed with noise. In an ideal situation, AC power system is supposed to have a constant frequency at specific voltage but owing to presence of connected nonlinear loads and injection into the grid from non-sinusoidal output active sources etc., have immensely contributed to the total distortion of the both current and voltage waveforms. This has increased the system loses and consequently affected other connected equipment in the system. Therefore there is a need to mitigate these effects if they cannot be eliminated intoto, hence the proposition of Kalman filter. It has been very useful in the aspect of electrical power discipline particularly in harmonic estimation. It has also find it way in the application of power system dynamics, optimal operation and control of motor, relay operation and protection, and also for accurate prediction of short and medium term electrical load forecasting. This paper is to highlight on the significant of Kalman filter methodological approach as adopted in electrical power system.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

Short-term residential electric load forecasting: A compressive spatio-temporal approach

Load forecasting is an essential step in power systems operations with important technical and economical impacts. Forecasting can be done both at aggregated and stand-alone levels. While forecasting at the aggregated level is a relatively easier task due to smoother load profiles, residential forecasting (stand-alone level) is a more challenging task due to existing diurnal, weekly, and annual cycles effects in the corresponding time series data and fluctuations caused by the random usage of appliances by end-users. Exploring the available historical load data, it has been discovered that there usually exists an interesting trend between the data from a target house and the data from its surrounding houses. This trend can be exploited for improving the forecast accuracy. One can define several different features for each house, including house size, occupancy level, and usage behavior of appliances. While the number of such features can be large, the main challenge is how to determine the best candidates (features) for an input set without increasing the forecasting computational costs. With this objective in mind, we present a forecasting approach which combines ideas from Compressive Sensing (CS) and data decomposition. The idea is to provide a framework which facilitates exploiting the existing low-dimensional structures governing the interactions among residential houses. The effectiveness of the proposed algorithm is evaluated using real data collected from residential houses in TX, USA. The comparisons against benchmark methods show that the proposed approach significantly improves the short-term forecasts.

A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies

Because of their excellent scheduling capabilities, artificial neural networks (ANNs) are becoming popular in short-term electric power system forecasting, which is essential for ensuring both efficient and reliable operations and full exploitation of electrical energy trading as well. For such a reason, this paper investigates the effectiveness of some of the newest designed algorithms in machine learning to train typical radial basis function (RBF) networks for 24-h electric load forecasting: support vector regression (SVR), extreme learning machines (ELMs), decay RBF neural networks (DRNNs), improves second order, and error correction, drawing some conclusions useful for practical implementations.

기온 데이터를 이용한 하계 단기전력수요예측

Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.

Short-term electricity load forecasting of buildings in microgrids

Electricity load forecasting plays a key role in operation of power systems. Since the penetration of distributed and renewable generation is increasingly growing in many countries, Short-Term Load Forecast (STLF) of micro-grids is also becoming an important task. A precise STLF of the micro-grid can enhance the management of its renewable and conventional resources and improve the economics of energy trade with electricity markets. As a consequence of the highly non-smooth and volatile behavior of the load time series in a micro-grid, its STLF is even a more complex process than that of a power system. For this purpose, a new prediction method is proposed in this paper, in which a Self-Recurrent Wavelet Neural Network (SRWNN) is applied as the forecast engine. Moreover, the Levenberg–Marquardt (LM) learning algorithm is implemented and adapted to train the SRWNN. In order to demonstrate the efficiency of the proposed method, it is examined on real-world hourly data of an educational building within a micro-grid. Comparisons with other load prediction methods are provided.

Neural network model ensembles for building-level electricity load forecasts

The future power grid is expected to provide unprecedented flexibility in how energy is generated, distributed, and managed, which increasingly necessitates an ability to perform accurate short-term small-scale electricity load and generation forecasting, e.g., at the level of individual buildings or sites. In this paper, we present a novel building-level neural network-based ensemble model for day-ahead electricity load forecasting and show that it outperforms the previously established best performing model, SARIMA, by up to 50%, in the context of load data from half a dozen operational commercial and industrial sites. In addition, we show a straightforward, automated way to select model parameters, making our model practical for use in real deployments.

An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting

Short-term electric load forecasting (STLF) is an essential tool for power generation planning, transmission dispatching, and day-to-day utility operations. A number of techniques are used and reported in the literature to build an accurate forecasting model. Out of them Artificial Neural Networks (ANN) are proven most promising technique for STLF model building. Many learning schemes are being used to boost the ANN performance with improved results. This motivated us to explore better optimization approaches to devise a more suitable prediction technique. In this study, we propose a new hybrid model for STLF by combining greater optimization ability of artificial bee colony (ABC) algorithm with ANN. The ABC is used as an alternative learning scheme to get optimized set of neuron connection weights for ANN. This formulation showed improved convergence rate without trapping into local minimum. Forecasting results obtained by this new approach have been presented and compared with other mature and competitive approaches, which confirms its applicability in forecasting domain.

Daily Load Forecasting Based on RBF-ARX Model

Short-term load forecasting plays a vital role for the power system’s safe operation and production arrangements. This paper regards electric load forecasting as a nonlinear time series prediction problem, and establishes the electric load autoregressive prediction model (ARX Model) based on historical load data, approaching ARX model parameters with RBF neural network, estimating model parameters with using a Structured Nonlinear Parameters Optimization Method (SNPOM), proposing cycle prediction method for short-term electric load forecasting. Experimental results show that the method has higher prediction accuracy for short-term electric load forecasting. Keywords: Short-term load forecasting; time series; RBF-ARX model; cycle forecasting; structured nonlinear parameter optimization method

Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm

STLF (short term electric load forecasting) plays an important role in the operation of power systems. In this paper, a new model based on combination of the WT (wavelet transform) and GM (grey model) is presented for STLF and is improved by PSO (particle swarm optimization) algorithm. In the proposed model, the weather data including mean temperature, mean relative humidity, mean wind speed, and previous days load data are considered as the model inputs. Also, the wavelet transform is used to eliminate the high frequency components of the previous days load data and improve the accuracy of prediction. To improve the accuracy of STLF, the generation coefficient of GM is enhanced using PSO algorithm. To verify its efficiency, the proposed method is used for New York's and Iran's load forecasting. Simulation results confirm favourable performance of the proposed method in comparison with the previous methods studied.

A novel hybrid model for bi-objective short-term electric load forecasting

Context: Current decision development in electricity market needs a variety of forecasting techniques to analysis the nature of electric load series. And the interpretability and forecasting accuracy of the electric load series are two main objectives when establishing the load forecasting model.Objective: Considering that electric load series exhibit repeating seasonal cycles at different level ( daily, weekly and annual seasonality), this paper concerns the interpretability of these seasonal cycles and the forecasting accuracy.Method: For the above proposes, the author firstly introduces a multiple linear regression model that involves treating all the seasonal cycles as the input attributes. The result helps the managers to interpret the series structure with multiple seasonal cycles. To improve the forecasting accuracy, a support vector regression model based on optimal training subset (OTS) and adaptive particle swarm optimization (APSO) algorithm is established to forecast the residual series. Thus, a novel hybrid model combining the proposed linear regression model and support vector regression model is built to achieve the above bi-objective short-term load forecasting.Results: The effectiveness of the hybrid model is evaluated by an electrical load forecasting in California electricity market. The proposed modeling algorithm generates not only the seasonal cycle's decomposition for the time series, but also better accuracy predictions.Conclusion: It is concluded that the hybrid model provides a very powerful tool of easy implementation for bi-objective short-term electric load forecasting.

Research in Residential Electricity Characteristics and Short-Term Load Forecasting

In this paper we make research in Residential short-term load forecasting. Different application scenes have different affecting factors of short-term load, so we should specifically analysis of factors that affect the load of the residential electricity. We use SPSS (Statistic Package for Social Science) to figure out the relationship between the daily load and temperature, weather conditions and other factors, finding the main factors among the impacting factors, and analyzing residential electricity consumption habits and load characteristics. Then, the paper introduces the common prediction methods. Combining with the above analysis to choose short-term load forecasting methods for residential users, we create automatic linear regression model and artificial neural network model to predict the future electricity load, calculating the residual between the predicted values and the actual values and mean square deviation of the values, and evaluating the accuracy of the load forecasting. The results prove that automatic linear regression model is effective in residential short-term electricity load forecasting.