Jeevan IJARCET VOL 1 ISSUE
Dr M.Veera Kumari
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Abstract
Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. Load forecasting plays an important role in power system planning, operation and control. Planning and operational applications of load forecasting requires a certain 'lead time' also called forecasting intervals. On the basis of lead time, load forecasts can be divided into four categories: very short-term forecasts, short-term forecasts, medium-term forecasts and long-term forecasts. In the present paper STLF is attempted and implemented using Matlab7.1 with evolutionary algorithms PSO and DE for the Andhra Pradesh Grid. The Load data of Andhra Pradesh grid of every month from the year 2007to 2011 was collected .Forecasted the Load data of next 5 years as one set and consecutive 5years as another set.
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Short term load forecasting using evolutionary algorithm for Tajikistan
International Journal of Power Electronics and Drive Systems, 2023
Load forecasting is a significant element in the energy management system of power systems. Precise load forecasting aids electric utilities to conduct decisions of unit commitment, reduction of spinning reserve capacity, and schedule device maintenance plan. Furthermore, load forecasting contributes to reducing the generation cost, and it is fundamental to the reliability of the power systems. On the other hand, short-term load forecasting is substantial for economic running. The forecasting precision directly affects the reliability, economy running and supplying power quality of the power system. Hence, finding the required load forecasting method to enhance the accuracy is valuable for forecasting precision. This paper proposed particle swarm optimization (PSO) to improve working support vector machine (SVM), SVM regression model is derived; also derived SVM with PSO. Support vector machine (SVM) model is adopted with and without PSO based on the historical load data and meteorological data of Tajikistan country, analysis the various factors affecting the forecast. The historical data and the load forecasting factors to be considered are normalized. The two parameters of SVM significantly influenced the model, and therefore it optimized using evolutionary algorithm.
A Long Term Load Forecasting of an Indian Grid for Power System Planning
Journal of The Institution of Engineers (India): Series B, 2014
This work incorporates the identification of model in functional form using curve fitting and genetic programming technique which can forecast present and future load requirement of an Indian grid. Here new fitness function has been used in the genetic algorithm, the forecasting result has been compared with other model to find suitability of a model in this regional environment.
Introduction to Load Forecasting
Load forecasting is a technique used by power companies to predict the power or energy needed to balance the supply and load demand at all the times. It is mandatory for proper functioning of electrical industry. It can be classified in terms of time like short-term (a few hours), medium-term (a few weeks up to a year) or long-term (over a year). In this paper, for medium and long term forecasting end use and econometric approach is used. Whereas for short term forecasting various approaches are used like regression models, time series, neural networks, statistical learning algorithms and fuzzy logic. Key Words:Load forecasting, regression models, time series, neural networks, statistical learning algorithms and fuzzy logic.
Comparative Analysis of Medium Term Load Forecasting using Genetic Algorithm and Particle Swarm Optimization
2018
The forecasting of electrical energy provides the required information about future conditions of the network to the system engineers and helps to predict essential improving actions such as putting power plants at their maximum production, electricity purchasing, switching etc. It is essential for the booking of fuel supply and maintenance activities and making arrangements for utility power exchange. With the ongoing advancement of new numerical, mining and man-made reasoning devices, it is potentially feasible to enhance the result.
Short-Term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms
IEEE Transactions on Power Systems, 2010
In this paper, fuzzy inductive reasoning (FIR) is applied to the problem of short-term load forecasting (STLF) in power systems for a day in advance. The FIR model learns both past and future relations from the load and the temperature. The proposed optimization model uses an evolutionary algorithm based on a local random controlled search-simulated rebounding algorithm (SRA)-to choose the inputs to the FIR model. Using an optimization method to determine linear and nonlinear relationships between the variables, a parsimonious set of input variables can be identified improving the accuracy of the forecast. The input variables are updated when a new load pattern is happened or when relative errors are unacceptable. With this update is achieved, a better monitoring of the load trend due to the process is not strictly stationary. The FIR and SRA methodology is applied to the Ecuadorian power system as an application example. Results and comparisons with other STLF methodologies (autoregressive integrated moving average, artificial neural networks, and adaptive neuro-fuzzy inference system) are shown, and conclusions are derived.
Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm
The major predicament with electricity as a means of transporting energy is that it cannot be stored unlike gas, oil, coal or hydrogen. Due to this, the electric power company faces economical and technical problems in planning, operation and control of electric power system. For the purpose of optimal planning and operation of an electric power system, there is need for appropriate evaluation of the present and future electric load. Electric load forecasting is used by electric power company to anticipate the amount of energy needed to meet up with the demand. Various statistical and artificial intelligence techniques have been applied to short term electric load forecasting in the past but were hampered with some drawbacks. This paper presents another approach for short term load forecasting with lead time of a day ahead (1-24 hours) using artificial neural network (ANN). The hidden layer in ANN model was generated using genetic algorithm instead of the usual practice of trial and error; the ANN model was trained by Levenberg Marquardt. The data (daily load data of 330/132/33KV substation, Ganmo, Kwara State for the month of May, 2014) used in training and validation of the neural network was obtained from the Transmission Company of Nigeria, National Control Centre, Osogbo, Osun State, Nigeria. The model for short term load forecast was designed and implemented with MATLAB package. The result was evaluated by Mean Absolute Percentage Error (MAPE) of 4.705 for the forecasted day.
Electric Load Forecasting Using Genetic Algorithm – A Review
IJMER
Many real-world problems from operations research and management science are very complex in nature and quite hard to solve by conventional optimization techniques. So, intelligent solutions based on genetic algorithm (GA), to solve these complicated practical problems in various sectors are becoming more and more widespread nowadays. GAs are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. This paper provides an overview of GAs, as well as their current use in the field of electric load forecasting. The types of GA are outlined, leading to a discussion of the various types and parameters of load forecasting. The paper concludes by sharing thoughts and estimations on GA for load forecasting for future prospects in this area. This review reveals that although still regarded as a novel methodology, GA technologies are shown to have matured to the point of offering real practical benefits in many of their applications.
Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization
2008 First International Conference on Emerging Trends in Engineering and Technology, 2008
Short term load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of power system. Artificial neural networks have long been proven as a very accurate non-linear mapper. ANN based STLF models generally use Back propagation algorithm which does not converge optimally & requires much longer time for training, which makes it difficult for real-time application. In this paper we propose a smaller MLPNN trained by Genetic algorithm & Particle swarm optimization. The GA training gives better accuracy than BP training, where as it takes much longer time. But the PSO training approach converges much faster than both the BP and GA, with a slight compromise in accuracy. This looks to be very suitable for real-time implementation. First International Conference on Emerging Trends in Engineering and Technology 978-0-7695-3267-7/08 $25.00
Load Forecasting Techniques for Power System: Research Challenges and Survey
IEEE Access
The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques among which hybrid methods are most popular. The present technologies of load forecasting and present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages are discussed in this paper. The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are compared and discussed with literature of different models to support the researchers to select the best model for load prediction. This comparison validates the fact that the hybrid forecasting models will provide a more optimal solution. INDEX TERMS Load forecasting, machine learning, load shedding, root mean squared error, mean absolute percentage error.
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Building an integrated hybrid model for short-term and mid-term load forecasting with genetic optimization
International Journal of Innovative Computing Information and Control, 2012
The enhancement of load forecasting has become one of the core research topics in the energy field. Because power load has both time-variant and nonlinear characteristics, different types of methods, neural networks (NN) in particular, have been applied to power load forecasting. This study proposes a real-valued genetic algorithm (RGA)based neural network with support vector machine (NN-SVM) model to predict the power load in both short-term and mid-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and RGA. The model consists of two stages. In short-term load forecasting (STLF), the first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast. Similar operations are used in mid-term load forecasting (MTLF). In the process of SVM training and NN learning, RGA is used to find the optimal parameters. The results of several experiments show that this new model performs more ...
A Study on Recent Trends for Load Forecasting with Artificial Intelligence
Turkish Online Journal of Qualitative Inquiry, 2021
In order to manage and maintain the power supply in distribution grids. The decision makers in the power grids must predict/forecast the energy demand with the least possibility of error. With the appropriate load forecasting, a stable, continuous and cost-effective power can be supplied to the consumers. Various factors such as load density, geographical factors, population growth, whether etc. can affect the accuracy & effectiveness of the load forecasting. Load forecasting is divided into three types: Long-Term load forecasting [LTLF], Medium-Term load forecasting [MTLF] and Short-Term load forecasting [STLF]. This paper gives an overview for load forecasting and its types. Out of which, STLF plays a very significant role in ensuring that power systems works efficiently, safely and economically. Various STLF techniques were proposed by the researchers that are discussed in literature survey, in order to optimize the distribution in electrical power grids. However, STLF is complex method as its prediction accuracy gets altered by the various complicated and non-linear external parameters. To overcome the drawbacks of STLF, a large number of STLF, MTLF and LTLF methods such as MLR, KBES etc. were proposed. From the literature survey conducted, it is observed that if these methods are incorporated with the artificial intelligence systemsalong with various dependency factors then the efficiency of these systems can further be increased. In the present work, Real time data of Haryana VidyutPrasaran Nigam Limited [HVPNL] has been used. The Forecasting is done using the various parameters and simulating the same using MATLAB and the results thus obtained have been compared with the actual load. The efficiency in Load Forecasting for all the three types i.e. Short Term, Medium Term and Long Term has been increased using the CNN network.
Short Term Load Forecasting Solution Methodologies: Literature Review 2013 Survey Paper
IOSR Journal of Electrical and Electronics Engineering, 2014
This paper presents the comprehensive study of the solution methodologies used so for the for Short Term Load Forecasting, these methodologies characterized by the methods and models as classical and artificial intelligent techniques .Statistical Technique includes Similar day approach, Linear regression, Time series method ,State space and intelligent techniques includes Artificial Neural Network (ANN),SVM(Support Vector Machine Regression), Fuzzy logic, SO(Particle Swarm Optimization),GA(Genetic Algorithm),ACA(Ant Colony Algorithm) and also there are hybrid techniques like Fuzzy and ANN, Adaptive neural fuzzy interface system, LS-SVM Optimized by Bacterial Colony Chemotax is Algorithm and GA based SVM and the last one is the Machine From this survey we show the importance of the short term load forecasting in operation and control and we conclude that the more promising method used for STFL is ANN(Artificial Neural Network) And A novel network Support Vector Machine and Ada Boost Regression techniques has great potential for the short term load forecasting other methods are also used for STFL.
Mid-term load forecasting of power systems by a new prediction method
Energy Conversion and Management, 2008
Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem.
Intelligent Machine Learning With Evolutionary Algorithm Based Short Term Load Forecasting in Power Systems
IEEE Access
Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load forecasting (STLF) plays a vital part in regulated power systems and electricity markets, which is commonly employed to predict the outcomes power failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) for power systems which involves different stages of operations such as data decomposition, data preprocessing, feature selection, prediction, and parameter tuning. Wavelet transform (WT) is used for the decomposition of the time series and Oppositional Artificial Fish Swarm Optimization algorithm (OAFSA) based feature selection technique to elect an optimal set of features. In order to improvise the convergence rate of AFSA, oppositional based learning (OBL) concept is integrated into it. Then, the water wave optimization (WWO) with Elman neural networks (ENN) model is employed for the predictive process. Finally, inverse WT is applied and obtained the hourly load forecasting data. To validate the effective predictive outcome of the IMLEA-STLF model, an extensive set of simulations take place on benchmark dataset. The resultant values ensured the promising results of the IMLEA-STLF model over the other compared methods.
Medium term load forecasting using evolutionary programming-least square support vector machine
ARPN journal of engineering and applied sciences, 2015
This paper presents new intelligent-based technique namely Evolutionary ProgrammingLeast-Square Support Vector Machine (EP-LSSVM) to forecast a medium term load demand. Medium-term electricity load forecasting is a difficult work since the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. Available historical load data are analyzed and appropriate features are selected for the model. Load demand in the year 2008 until 2010 are used for features in combination with day in months and hour in days. There are 3 inputs vectors for this proposed model consists of day, month and year. As for the output, there are 24 outputs vectors for this model which represents the number of hour in a day. In EP-LSSVM, the Radial Basis Function (RBF) Kernel parameters are optimally selected using Evolutionary Programming (EP) optimization technique for accurate prediction. The performance of EP-LSSVM is compared with those ...
Short term load forecasting using artificial neural network: a comparison with genetic algorithm implementation
Journal of Engineering …, 2009
Accurate load forecasting holds a great saving potential for electric utility corporations since it determines its main source of income, particularly in the case of distributors. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. It is therefore necessary that the electricity generating organizations should have prior knowledge of future demand with great accuracy. Some data mining algorithms play the greater role to predict the load forecasting. This research work examines and analyzes the use of artificial neural networks (ANN) and genetic algorithm (GA) as forecasting tools for predicting the load demand for three days ahead and comparing the results. Specifically, the ability of neural network (NN) models and genetic algorithm based neural networks (GA-NN) models to predict future electricity load demand is tested by implementing two different techniques such as back propagation algorithm and genetic algorithm based back propagation algorithm (GA-BPN).
Long-term Load Forecasting for Optimal Power System Planning and Decision-Making
Journal of Engineering Research and Reports, 2024
Forecasting the future load growth of an area based on its load demand is often a proactive measure to ensure a steady electricity power supply to that area. The study focused on long-term load forecasting for power system planning, specifically examining the electric load demand from consumers on distribution transformers within Port Harcourt City, located in Rivers State, Nigeria. The study encompassed a comprehensive review of both statistical and artificial intelligence-based approaches. Historical load data for distribution transformer readings spanning 2008 to 2017 were acquired from the Port Harcourt Electricity Distribution Company (PHEDC) and subjected to analysis using the curve-fitting technique. For the period between 2015 and 2030, a yearly load forecast simulation was conducted using the Fourier Series model, implemented with MATLAB software. This simulation aimed to provide insights into future load demand, facilitating careful and informed decision-making in the investment, operation, and maintenance of power system equipment. The effectiveness of the forecasting investigation was assessed using the Root Mean
An Introduction to Load Forecasting: Conventional and Modern Technologies
The deregulation of the power system industry has made short term load forecasting increasingly important. Short-term load forecasting (S TLF) is of great importance for the safety and stabilization of grids. In this paper I will be introducing some important things which one need to know before working on load forecasting. What methods were conventionally used and what was the necessity for new approaches and how they are better. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. An extensive survey of ANN-based load forecasting models is studied. Various important factors which one need to know before working on load forecasting are discussed briefly in this paper.
A Data Driven Approach for Day Ahead Short Term Load Forecasting
IEEE Access
This paper aims to develop an evolutionary deep learning based hybrid data driven approach for short term load forecasting (STLF) in the context of Bangladesh. With the lapse of time, the power system is getting complex. Penetration of intermittent renewable energy (RE) into the grid, changing prosumer load pattern with the need of demand side management (DSM) has thrown a challenge for dynamic power system operation and control. Load forecasting plays a significant role in this dynamic operation and control. In addition, it directly affects the future planning of network expansion, unit commitment and economic energy mix for power market. Day ahead short-term forecasting is very crucial for day to day operation. As such, various conventional and modified methods have been used over time for short-term prediction. Nevertheless, the existing approaches like age old statistical methods, artificial intelligence (AI), machine learning (ML), deep learning (DL) techniques alone cannot provide effective accuracy all the time. Hence, an integrated genetic algorithm (GA)-bidirectional gated recurrent unit (Bi-GRU) hybrid data driven technique (GA-BiGRU) is proposed in this work. The developed method is validated in Bangladesh power system (BPS) network by providing day ahead forecasting of electrical load of the whole country. Besides, the performance of the prediction model is compared with some existing approaches such as long short-term memory network (LSTM), gated recurrent unit (GRU) and integrated genetic algorithm-gated recurrent unit (GA-GRU) in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The outcome gives an indication of better forecasting accuracy of proposed GA-BiGRU evolutionary DL technique compared to others. INDEX TERMS Short-term load forecasting, bidirectional gated recurrent unit, GA-BiGRU, Bangladesh power system, genetic algorithm, demand side management, MAPE, RMSE.
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