Peak Load Forecasting Research Articles

Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

Purpose The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques. Design/methodology/approach Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R2) indices are used to evaluate forecasts. Findings The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R2 above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector. Originality/value This is original research.

Short term electricity load forecasting for institutional buildings

Peak load demand forecasting is important in building unit sectors, as climate change, technological development, and energy policies are causing an increase in peak demand. Thus, accurate peak load forecasting is a critical role in preventing a blackout or loss of energy. This paper presents a study forecasting peak load demand for an institutional building in Seoul. The dataset were collected from campus area consisting of 23 buildings. ARIMA models, ARIMA-GARCH models, multiple seasonal exponential smoothing, and ANN models are used. We find an optimal model with moving window simulations and step-ahead forecasts. Also, including weather and holiday variables is crucial to predict peak load demand. The ANN model with external variables (NARX) worked best for 1-h to 1-d ahead forecasting.

Daily Peak Load Forecast Using Artificial Neural Network

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load

This paper presents a new probabilistic building load forecasting model considering uncertainties in weather forecasts and abnormal peak load. Two basis function components are developed, including the probabilistic normal load forecasting and the probabilistic uncertain peak load (or abnormal peak load) forecasting. The probabilistic normal load forecasting model is built using the artificial neural network (ANN) and the probabilistic temperature forecasts. The probabilistic abnormal peak load forecasting model consists of two models quantifying the probabilistic occurrence and magnitude of the peak abnormal differential load respectively. The test results show that the ANN deterministic load forecasting model can achieve satisfactory performance with the average mean absolute percentage error (MAPE) of 5.0%. The probabilistic occurrence model can forecast the occurrence frequency of the peak abnormal differential load with the satisfactory agreement, and the probabilistic magnitude model can well forecast the magnitudes of the peak abnormal differential load with the Kolmogorov-Smirnov error of 0.09. Real-time application case studies are conducted by different means of using the probabilistic weather forecasts. The results show that the probabilistic normal load forecasts have satisfactory accuracies and the load forecasts based on the one-day-ahead probabilistic weather forecasts are the best.

Deep Learning for Daily Peak Load Forecasting–A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping

Daily peak load forecasting is an essential tool for decision making in power system operation and planning. However, the daily peak load is a nonlinear, nonstationary, and volatile time series, which makes it difficult to be forecasted accurately. This paper, for the first time, proposes a bespoke gated recurrent neural network combining dynamic time warping (DTW) for accurate daily peak load forecasting. The shape-based DTW distance is used to match the most similar load curve, which can capture trends in load changes. By analyzing the relationship between the load curve and the cycle of human social activities, the some-hot encoding scheme is first applied on the discrete variables to expand the features to further characterize their impact on load curves. Then, a three-layer gated recurrent neural network is developed to forecast daily peak load. The proposed algorithm is implemented on the Theano deep learning platform and tested on the loaded dataset of the European Network on Intelligent Technologies. The simulation results show that the proposed algorithm achieves satisfactory results compared with other algorithms using the same dataset in this paper.

Daily Peak Load Forecasting by Artificial Neural Network using Differential Evolutionary Particle Swarm Optimization Considering Outliers

Abstract This paper proposes an Artificial Neural Network (ANN) based daily peak load forecasting method by differential evolutionary particle swarm optimization (DEEPSO) considering outliers. When outliers exist in the training data, forecasting accuracy of daily peak load forecasting can be affected by the outliers. Therefore, engineers have removed the outliers from training data so far and it is a heavy burden for engineers. Utilization of evolutionary computation has a possibility to solve this problem. Moreover, forecasting accuracy may be improved using evolutionary computation techniques instead of the conventional stochastic gradient descent (SGD) with outliers. The proposed weights tuning method by DEEPSO is compared with the conventional weights tuning methods by SGD and PSO for verification of the efficacy of the proposed method.

Distribution feeder‐level day‐ahead peak load forecasting methods and comparative study

Day-ahead peak load forecasting is a crucial factor for the electrical utility for daily power planning and distribution. A comparative study of the peak load forecasting at distribution feeder-level circuits with high penetration level of PV generation are investigated. First, two years of private load data from the local utility and two years of public load data from Texas utility are analysed. The correlation analysis is applied to peak load and its driver factors. Next, Bayesian additive regression trees (BART) is employed to do peak load forecasting. Since the BART method takes an amount of time to generate the forecasting, the composite kernel methods based on Gaussian process regression (CKGPR) are designed. Then these methods are compared with multiple linear regression method and the support vector regression method based on the residential area and the business area load data. Thorough comparison results are presented based on five forecasting measurements. The BART has the best forecasting accuracy among all the indices, and the CKGPR also has counterpart forecasting results but with less computation time. Meanwhile, the forecasting accuracy difference between two areas is analysed. Lastly, influential driver factors are summarised.

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM) is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm), SVM (Support Vector Machine) and BP neural network to compare with the CEEMDAN-MGWO-SVM model and analyze the forecasting results of the same sample data. The experimental results fully demonstrate the reliability and effectiveness of the CEEMDAN-MGWO-SVM model proposed in this paper for daily peak load forecasting, which shows the strong generalization ability and robustness of the model.

Day-ahead Base, Intermediate, and Peak Load Forecasting using K-Means and Artificial Neural Networks

Industries depend heavily on the capacity and availability of electric power. A typical load curve has three parts, namely, base, intermediate, and peak load. Predicting the three (3) system loads accurately in a power system will help power utilities ensure the availability of the supply and to avoid the risk for over- or under- utilization of generation, transmission, and distribution facilities. The goal of this research is to create a suitable model for day-ahead base, intermediate and peak load forecasting of the electric load data provided by a power utility company. This paper presents an approach in predicting the three (3) system loads using K-means clustering and artificial neural networks (ANN). The power utility’s load data was clustered using K-means to determine the daily base, intermediate and peak loads that were then fed into an ANN model that utilized Quick Propagation training algorithm and Gaussian activation function. It was found out that the implemented ANN model generated 2.2%, 1.84%, and 1.4% as the lowest MAPE for base, intermediate, and peak loads, respectively, with highest MAPE below the accepted standard error rate of 5%. The results of this study clearly suggest that with the proper method of data preparation, clustering, and model implementation, ANN can be a viable solution in forecasting the day-ahead base, intermediate, and peak load demand of a power utility.

A Probabilistic Approach Considering Contingency Parameters for Peak Load Demand Forecasting

Accurate load forecasting is a critical step for power system generation planning. Contingency parameters of the system and their dynamic characteristics should be taken into account for the purpose of load forecasting. In this paper, a probabilistic load forecasting algorithm considering contingency parameters is developed for the peak load forecasting. Using the chi-square distribution test and historical data, the probabilistic distribution of contingency parameters can be determined. In a case study, the Monte Carlo simulation is run to forecast load demand and generation scenarios of Bangladesh based on the developed adaptive algorithm and the calculated probabilistic distribution. The influence of contingency parameters is evaluated using a Bayesian network in a sensitivity study.

Long-term forecasting of annual peak load considering effects of demand-side programs

The main purpose of this research paper is to investigate the long-term effects of the proposed demand-side program, and its impact on annual peak load forecasting important for strategic network planning. The program comprises a particular set of demand-side measures aimed at reducing the annual peak load. The paper also presents the program simulations for the case study of the Electricity Distribution Company of Belgrade (EDB). According to the methodology used, the first step is to determine the available controllable load of the distribution utility/area under consideration. The controllable load is presumed constant over the analyzed time horizon, and the smart grid (SG) infrastructure available. The saturation of positive effects during intense program application is also taken into account. Technical and economic input data are taken from the real projects. The conducted calculations indicate that demand-side programs can bring about the same results as the energy storage in the grids with a strong impact of distributed generation from variable renewable sources (V-RES). In conclusion, the proposed demand-side program is a good alternative to building new power facilities, which can postpone investment costs for a considerable period of time.

Integrating long-term economic scenarios into peak load forecasting: An application to Spain

The treatment of trend components in electricity demand is critical for long-term peak load forecasting. When forecasting high frequency variables, like daily or hourly loads, a typical problem is how to make long-term scenarios - regarding demographics, GDP growth, etc. - compatible with short-term projections. Traditional procedures that apply de-trending methods are unable to simulate forecasts under alternative long-term scenarios. On the other hand, existing models that allow for changes in long-term trends tend to be characterized by end-of-year discontinuities. In this paper a novel forecasting procedure is presented that improves upon these approaches and is able to combine long and short-term features by employing temporal disaggregation techniques. This method is applied to forecast electricity load for Spain and its performance is compared to that of a nonlinear autoregressive neural network with exogenous inputs. Our proposed procedure is flexible enough to be applied to different scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections.

Identification of nonlinear Hammerstein system using mixed integer-real coded particle swarm optimization: application to the electric daily peak-load forecasting

This paper investigates the modeling of a class of dynamic systems using nonlinear Hammerstein (NLH) model composed of a memory-less polynomial block cascaded to an autoregressive with exogenous input (ARX) time-series block. The model thus defined is known as NLHARX. Both the integer orders and the real coefficients of the model are identified simultaneously in a unified framework using a new algorithm based on a mixed coded integer-real particle swarm optimization. Unlike classical identification methods which assume the orders to be known in advance, the proposed approach is new since it estimates both the real and integer design parameters while minimizing the error between the outputs of the system and the model. The usefulness and the effectiveness of the proposed approach have been demonstrated through extensive simulations. Two illustrative examples are included in this paper: an empirical example and an application to the forecasting of the daily peak-load of Hail region, Saudi Arabia. Future works will be devoted to the identification of more complex dynamic systems, such as Hammerstein–Wiener and the application to the prediction of time-series related to water and energy.

Daily Peak Load Forecasting by Taguchi's T Method

SUMMARYIn this paper, a new technique of daily peak load forecasting is proposed. At first, we propose nonlinear correction T method as improvement technique of T method that is one of the multivariate analysis techniques, called Mahalanobis–Taguchi (MT) system, suggested in Quality Control. Moreover, we examine the daily peak load forecasting technique, which is based on data in the last year on a forecasting day, by T method and nonlinear correction T method. Furthermore, we examine the technique, which is based on the data of most recent several weeks of a forecasting day, by the T method and Nonlinear correction T method. In addition, the effectiveness of the proposed method by comparing the multiple regression analysis is discussed.

Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection

Electricity demand (kilowatt hour: kWh) and peak power load (kilowatt: kW) forecasting is very important for not only expansion planning purposes (long term), but also for dispatching purposes (short term). Hence, from the long term forecasting perspective to the very short term forecasting perspective, the nature of electricity demand and the peak power load forecasting has to be studied and understood very well. At first, the problem has to be understood very well, then the solution of this problem has to be studied and solved. These activities are in the scope of this research, development, demonstration, & deployment (RD 3 ) studies. The author thinks that the natural mechanisms of electricity demand and peak power load forecasting problem can be understood very well by finding, defining, identifying, and describing the factors (parameters, variables) that affect the electricity demand and peak power load. In this study, GATE is only used during corpus development as a backup check. R text mining package (Rtm) and TextSTAT are used as main text mining and analysis tools. 314 terms as candidate variable terms are found by this text analysis. Afterwards, all variables are studied and analyzed by a grey based natural reasoning with simple weighted average approach (WA) (only for long term factors as preliminary in this application) (on way of simple additive weighting method: SAW). Finally, 43 terms (e. g. population, weather, climate, economy, price) for variables are found for infant and mature RD 3 studies of 100% renewable energy (RE) worldwide grid (Global Grid). Findings of this study can also be used in other grid types. It is believed that a specific dictionary and encyclopedia in this particular subject should be developed for researchers common sense which will also help building of the Global Grid Prediction Systems (G 2 PS).

Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria

This paper presents an approach for long-term estimation and forecasting of electric peak load. A 10-year peak load forecast is performed on Uyo transmission substation in Akwa Ibom State, Nigeria. The peak loads of the past ten years (from 2006 to 2010) are used as input data used to develop the model for forecasting the peak load demand in Uyo metropolis. Particularly, Multiple Linear Regression (MLR) method is used to model the annual peak load. The explanatory variables, namely, temperature, population and gross domestic product are used in the analysis. The peak load model parameters are estimated using only the data of the year 2006 to the year 2012, which accounts for 70% of the entire dataset for training and 30% (that is, 2013 to 2015) of the data are used for cross validation. The results show that with respect to the training dataset the prediction model has Mean Absolute Percentage Error (MAPE) of 0.00613%, Mean Absolute Deviation (MAD) of 0.277743 and Coefficient of Determination (R2) value of 0.99184 which shows that about 99.184% of the peak load are explained by the explanatory variables used in the prediction. Furthermore, with respect to the validation dataset (2013 to 2015) the prediction model has RMSE of 1.038042 and percentage error of less that 2% which shows that the proposed peak-load-demand model can effectively predict the peak load demand for Uyo.

Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation

The National Electricity Company (PT PLN) should have an estimated peak load of the substation transformer in the future. This is useful to be able to achieve transformer capability and can be used as a first step to anticipate the possibility of replacement of a new transformer. This research presents a peak load forecasting system transformer1 and transformer2 in Bumiayu substation using Backpropagation Artificial Neural Network (ANN). This study includes the procedures for establishing a network model and manufacture forecasting system based GUI (Graphic User Interface) using MATLAB 2015a. The formation of the network model refers to input variables consisting of GRDP data, population data and historical data of peak load of transformer. In this research, a multilayer network model, which consists of 1 input layer, 2 hidden layers and 1 output layer, is used. The peak load forecasting of transformer1 produces 5.7593e-08 for training MSE and 5.3784e-04 for testing MSE. Meanwhile, forecasting the peak load transformer2 generated 3.3433e-08 for training MSE and 9,4710e-04 for testing MSE.

Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection

Electricity demand (kilowatt hour: kWh) and peak power load (kilowatt: kW) forecasting is very important for not only expansion planning purposes (long term), but also for dispatching purposes (short term). Hence, from the long term forecasting perspective to the very short term forecasting perspective, the nature of electricity demand and the peak power load forecasting has to be studied and understood very well. At first, the problem has to be understood very well, then the solution of this problem has to be studied and solved. These activities are in the scope of this research, development, demonstration, & deployment (RD³) studies. The author thinks that the natural mechanisms of electricity demand and peak power load forecasting problem can be understood very well by finding, defining, identifying, and describing the factors (parameters, variables) that affect the electricity demand and peak power load. In this study, GATE is only used during corpus development as a backup check. R text mining package (Rtm) and TextSTAT are used as main text mining and analysis tools. 314 terms as candidate variable terms are found by this text analysis. Afterwards, all variables are studied and analyzed by a grey based natural reasoning with simple weighted average approach (WA) (only for long term factors as preliminary in this application) (on way of simple additive weighting method: SAW). Finally, 43 terms (e. g. population, weather, climate, economy, price) for variables are found for infant and mature RD³ studies of 100% renewable energy (RE) worldwide grid (Global Grid). Findings of this study can also be used in other grid types. It is believed that a specific dictionary and encyclopedia in this particular subject should be developed for researchers common sense which will also help building of the Global Grid Prediction Systems (G²PS).

DGM (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting

A large number of renewable energies and uncertain power load accessing electric power system make the power load forecasting more complicated and face more new challenges. This paper presents a hybrid annual peak load forecasting model [namely MVO-DGM (1, 1)], which employs the latest optimization algorithm MVO (multi-verse optimizer) to determine two parameters of DGM (1, 1) model, and then uses the optimized DGM (1, 1) model to forecast annual peak load. The annual peak load of Shandong province in China from 2005 to 2014 is selected as the empirical example, and the analysis results demonstrate that the MVO algorithm for parameters’ determination of DGM (1, 1) model has significant superiority over the least square estimation method, particle swarm optimization and fruit fly optimization algorithm in terms of annual peak load forecasting. In addition, the proposed MVO-DGM (1, 1) peak load forecasting model has more excellent forecasting performance than other non-optimized forecasting techniques and other optimized DGM (1, 1) models due to its ascended local optima avoidance and better convergence speed. The hybrid MVO-DGM (1, 1) model proposed in this paper is feasible and effective in annual peak load forecasting, which can improve the forecasting accuracy.

Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily short-term peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.