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The accuracy of electric load forecasts significantly affects the overall performance of power system. Some time due to complicated load pattern, forecasting becomes difficult. The object of this study is to develop more effective forecasting models, among others. This paper compares the electric load forecasting accuracy of ANN based techniques. This study investigates the time series techniques...
The use of Artificial Neural Networks (ANN) by power distribution companies has gained a wide reception due to its ability to predict close to accurate forecasted electric load consumption. A local power utility company in the Philippines has existing data of its electric load consumption however there is no ANN model that can process this data to produce close to accurate forecasted load which is...
Electrical energy consumption is affected by many parameters. These includes the variables related to power system itself, weather and climatic factors and socio-economic being of the energy consumers. In this paper, two components of load forecasting are classified. The parameters that influence the energy consumption and the methods used to forecast the energy consumption are reviewed. It is observed...
This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems (HEMSs) to use more energy in low price periods in order to reduce their electricity procurement cost...
Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading...
The Quality factors of an empty and loaded reverberant cavity were measured using time domain techniques. Measurements were performed for a set of frequencies under different loading conditions achieved by varying the material type and material amount. The measured data were used to develop an artificial neural network (ANN) model that predicts the amount of material required for a desired change...
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
By influencing the demand side by means of price signals (Demand Response) additional flexibility potential in electric supply systems can be provided. However, by influencing the demand side typical consumption patterns of previously unaffected consumers are changed. This will lead to increasing uncertainty in load forecasting. This paper deals with the forecast of load time series in consideration...
Machine learning methods are the main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand,...
The recent interest in smart grid vision enables several smart applications in different parts of the power grid structure, where specific importance should be given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases...
This paper presents modeling techniques for submerged membrane bioreactor (SMBR) filtration process using. The Nonlinear Auto Regressive with Exogenous Input (NARX) structure was used with adaptive neuro-fuzzy interface system (ANFIS) and feed forward neural network (FFNN) are employed to model the filtration system. The transmembrane pressure and the permeate flux were model during the relaxation...
This paper shows the application of the resource allocation network (RAN) algorithm to the problem of electrical load forecasting in a Spanish utility company. The choice of the parameters of the algorithm is usually done manually. In this paper the possibility of automatic selection of parameters is investigated. These parameters are of paramount importance since they determine the final size of...
Constant tariff scheme produces a large and continuously-changing difference between electricity cost and price. Consequently, the concern of power system planning and economic generation becomes significant. To overcome this problem accurate load forecasting is a field of immense importance. Conventional methods, i.e., Moving Average (MA) and Holt-Winter (HW) methods are inappropriate to forecast...
Electric load forecasting is essential for effective power system planning and operation because many decisions related to power system planning and operation depends on future behavior of electric loads. In this paper, we present automatic TSK fuzzy model extraction method for one hour ahead load forecasting. To extract TSK fuzzy model automatically, cooperative random learning particle swarm optimization...
This paper presents a medium term load forecasting methodology based on a mixed statistical computational intelligence model. The methodology can be used by any entity (such as transmission and distribution operators, electricity suppliers or energy managers) interested in planning different activities with electricity. The methodology produces daily load profiles forecasts for all the 365 days of...
The current UK Smart Metering Technical Specification requires smart meter readings to be collected once a day, primarily to support accurate billing without violating users' privacy. In this paper we consider the use of Smart Metering data for Distribution State Estimation (DSE), and compare the effectiveness of daily data collection strategy with a more frequent, half-hourly SM data collection strategy...
Short term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season...
Accurate and robust load forecasting models play an important role in power system planning. Due to smaller size and inherent property of good classification, Radial Basis Function Neural Network (RBFNN) is always preferred over other neural network structures. It is used by researchers as an effective tool for Short-Term Load Forecasting (STLF). The smaller size of this network may lead its output...
Management and pricing of electricity in power system is largely influenced by Short-Term Load Forecasting (STLF). This paper presents a hybrid algorithm, where Radial Basis Function Neural Network (RBFNN) is optimized using Genetic Algorithm (GA) for STLF, with load and day-type as input parameters. Since, conventional training methods, viz., principle component analysis and least square method,...
This paper highlights input variable selection for neural network of the electrical system to predict the load demand in 168 hours ahead. Autocorrelation (ACF), partial autocorrelation (PACF) and cross correlation (CCF) analysis are used to identify the correlated input for the forecast model. The combination of time, time indicator, lagged load and respective weather variables are considered as forecast...
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