North-American Electric Utility Research Articles

Hybrid filter–wrapper feature selection for short-term load forecasting

Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate pool of features, coupled with the general lack of success in employing filter or wrapper methods individually, in this study we propose a hybrid filter–wrapper approach for STLF feature selection. This proposed approach, which is believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method, implemented via a firefly algorithm, to further reduce the redundant features without degrading the forecasting accuracy. The well-established support vector regression is selected as the modeler to implement the proposed hybrid feature selection scheme. Real-world electricity load datasets from a North-American electric utility and the Global Energy Forecasting Competition 2012 have been used to test the performance of the proposed approach, and the experimental results show its superiority over selected counterparts.

Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequencies. Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. Compared to the conventional neuro-evolution method, ELM-MABC can improve the learning accuracy with fewer iteration steps. The proposed method is tested on two datasets: ISO New England data and North American electric utility data. Numerical testing shows that the proposed method can obtain superior results as compared to other standard and state-of-the-art methods.

The Effects of Mobile Computer Location in a Vehicle Cab on Muscle Activity and Body Posture of Large and Small Drivers

The objective of this research is to determine the best location to place a conventional mobile PC supported by a commercially available mount in a vehicle cab for small and large body sizes. Many North American electric utility companies and sales agencies are in the process of integrating mobile computers into their fleet vehicle cabs. Four locations of mobile computers in a light truck cab were tested in a laboratory study to determine how driver body size and computer location affected muscle activity of the lower back and shoulders and joint angles of the shoulders, elbows, and wrists of small and large drivers. Driver size had a negligible effect on biomechanical loading. Regardless of driver size, placing the mobile computer closer to the steering wheel reduced low back and shoulder muscle activity required to use the mobile computer, and joint angles were also closer to neutral. Also locating the mobile computer close to the steering wheel reduces risk factors of injuries such as low back pain and shoulder tendonitis. A recommendation for the initial location along with adjustments to locate the mobile computer with respect to the steering wheel is included.

Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction

In this paper, WESN (wavelet echo state network) with a novel ESN-based reconstruction stage is applied to both STLF (short-term load forecasting) and STTF (short-term temperature forecasting). Wavelet transform is used as the front stage for multi-resolution decomposition of load or temperature time series. ESNs function as forecasters for decomposed components. A modified shuffled frog leaping algorithm is used for optimizing ESNs. Both one-hour and 24-h ahead predictions are studied where the number of inputs are kept minimum. The performance of the proposed WESN-based load forecasters are investigated for three cases as the predicted temperature input is fed by actual temperatures, output of the WESN-based temperature forecasters and noisy temperatures. Effects of temperature errors on load forecasts are studied locally by sensitivity analysis. Hourly loads and temperatures of a North-American electric utility are used for this study. First, results of the proposed forecasters are compared with those of ESN-based forecasters that have previously shown high capability as stand-alone forecasters. Next, the WESN-based forecasters are compared with other models either previously tested on the data used here or to be rebuilt for testing on these data. Comparisons reveal significant improvements on accuracy of both STLF and STTF using the proposed forecasters.

Application of echo state networks in short-term electric load forecasting

The paper presents the application of echo state network (ESN) to short-term load forecasting (STLF) problem in power systems for both 1-h and 24-h ahead predictions while using the least number of inputs: current-hour load, predicted target-hour temperature, and only for 24-h ahead forecasting, day-type index. The study is much attractive due to inclusion of weekends/holidays what makes STLF problem much more difficult. The main aim is to show the great capabilities of ESN as a stand-alone forecaster to learn complex dynamics of hourly electric load time series and forecast the near future loads with high accuracies. ESN as the state-of-the-art recurrent neural network (RNN) gains a reservoir of dynamics tapped by trained output units with a simple and fast single-stage training process. Furthermore, the application of ESN to predict the target-hour temperature needed by ESN-based load forecasters is examined. Since temperature prediction errors affect load forecasting accuracy, effects of such errors on ESN-based load forecasting are studied by both sensitivity analysis and applying noisy temperature series. Real hourly load and temperature data of a North-American electric utility is used as the data set. The results reflect that the ESN-based STLF method provides load forecasts with acceptable high accuracy.

Short-term load forecasting of power systems by gene expression programming

Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is utilized to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the short-term load. The GEP-based formulas are relatively short, simple and particularly valuable for providing an analysis tool accessible to practicing engineers.

A hierarchical hybrid neural model with time integrators in long-term load forecasting

A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets—one on top of the other—and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a mutilated architecture of it, and to a multilayer perceptron. The hierarchical, the mutilated, and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. The results from the experiments show that the performance of HHNM on long-term load forecasts is better than that of the mutilated model, and much better than that of the MLP model.

A hierarchical neural model with time windows in long-term electrical load forecasting

A novel hierarchical hybrid neural model to the problem of long-term electrical load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets: one on top of the other, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are endowed with time windows in their input layers. They are trained and assessed on load data extracted from a North-American electric utility. The models are required to predict once every week the electric peak-load and mean-load for the next 2 years. The results are presented and evaluated in the paper.

Alternative Regulation for North American Electric Utilities

After a decade of favorable operating conditions, utilities find themselves faced with accelerating prices for key inputs and a growing need for new capacity. These pressures are likely to prompt increasingly frequent, and perhaps more contentious, rate cases. Steady progress in the development of alternative regulation provides hope that the utility industry will respond to these challenges much better than in 1975-85.

Long-term load forecasting via a hierarchical neural model with time integrators

A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets – one on top of the other –, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper.

Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting

The importance of short-term load forecasting has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and nonstationarity of loads are becoming worse, due to the dynamics of energy prices. Besides, the number of nodal loads to be predicted does not allow frequent interactions with load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. This paper describes two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting. Its main goal is to develop more robust load forecasters. Hourly load and temperature data for North American and Slovakian electric utilities have been used to test the proposed methodology.

Aplicação da transformada wavelet discreta na previsão de carga a curto prazo via redes neurais

A importância da previsão de carga a curto prazo tem crescido ultimamente. Com a desregulamentação do setor elétrico e a competição advinda desse processo, a previsão do preço de energia se transformou em uma atividade bastante atraente. A previsão das cargas das diversas barras é essencial para alimentar os métodos analíticos utilizados na determinação dos preços da energia. A variabilidade e a não estacionariedade das cargas estão ficando cada vez mais acentuadas devido à dinâmica das tarifas de energia. Além disso, o número de pontos de carga a serem previstos não permite intervenções freqüentes dos especialistas em previsão. Portanto, previsores de carga mais autônomos são necessários nesse novo cenário competitivo. Este artigo propõe uma nova técnica para a previsão de carga a curto prazo baseada na transformada wavelet discreta. O objetivo principal é desenvolver um previsor neural de carga mais robusto. Dois anos completos de dados de carga de uma concessionária de energia elétrica norte-americana foram utilizados para testar a metodologia proposta.

Environmental performance benchmarking for electric utilities

AbstractElectric utility companies in the United States and Canada are participating in a unique environmental benchmarking program (EBP) designed to comparatively assess environmental performance. Each company annually provides data on emissionslwastes, compliance, and other environmental aspects to an independent consultant, Research Triangle Institute (RTI), whose staff compiles the data and computes approximately 80 discrete environmental performance metrics for each company. RTI provides a confidential report for each participant presenting the results of the assessment. In addition, RTI conducts a follow‐up assessment to determine “best‐practices” of each of the top performers for each metric. The EBP provides each participant with useful information on its strengths and weaknesses relative to the other companies in the program as well as ideas on how to improve its environmental performance. Annual participation in the program allows a company to measure improvement in performance on an annual basis. This article summarizes the evolution of the EBP and describes the study methods and reporting approach. Our goal in sharing this information is to demonstrate the usefulness of the program and encourage further participation by all major North American electric utilities.

A survey of North American electric utility concerns regarding nonsinusoidal waveforms

50 utilities in the United States and Canada responded to a questionnaire survey regarding concerns about nonsinusoidal waveforms in the electric distribution network. Responses regarding instrumentation, apparent and reactive power definitions, meter errors, rates, and trends are presented and discussed.