Short-Term Load Forecasting for Peak Load Reduction Using Artificial Neural Network Technique

Ganguly, Ayandeep; Goswami, Kuheli; Mukherjee, Arpita; Sil, Arindam Kumar

doi:10.1007/978-981-13-3122-0_56

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…In this article, we have compared different forecasting tool and finally used auto regressive integrated moving average with exogenous variables (ARIMAX), a multivariate method to perform load forecasting, which is performing comparatively better than other techniques [36]. By using this classical method, we have achieved an almost error free forecasted demand profile [37], [38].…”

Section: System Modellingmentioning

confidence: 99%

Renewable energy based dynamic tariff system for domestic load management

Goswami

Sil

2022

IJEECS

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To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.

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Section: System Modellingmentioning

confidence: 99%

Renewable energy based dynamic tariff system for domestic load management

Goswami

Sil

2022

IJEECS

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“…The short-term load forecasting is used by the supply authority for operational purposes such as unit commitment, economic dispatch, load flow, frequency control, security, and reliability of the system (Srivastava et al, 2016). The medium-term load forecasting predicts the load demand that provides information for system planning and operation while long term load forecasting is mainly used for power system planning (Ganguly et al, 2019;Peng et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Ibrahim¹,

Owonikoko²,

Abdulkarim³

et al. 2021

JST

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A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

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“…Typically, the proposed forecasting methods are applied to determine production capability or power generation, distribution, program planning, and maintenance schedules, etc. [44,45]. The SOTLF is essential to control and plan for the DP shipboard power system, which can be used as input for the power flow or fault analyses.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

Mehrzadi

Terriche

et al. 2020

Applied Sciences

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The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters’ power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental disturbances. The proposed method evaluates with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Numerical analysis has shown that with the proposed method, the future DP load behavior can be predicted more accurately than that obtained from the traditional methods, which greatly assists in operation and planning of power system to maintain system stability, security, reliability, and economics.

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Short-Term Load Forecasting for Peak Load Reduction Using Artificial Neural Network Technique

Cited by 8 publications

References 9 publications

Renewable energy based dynamic tariff system for domestic load management

Renewable energy based dynamic tariff system for domestic load management

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

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