Day ahead hourly load forecast of PJM electricity market and iso new england market by using artificial neural network

Sahay, Kishan Bhushan; Tripathi, M. M.

doi:10.1109/isgt.2014.6816486

Cited by 34 publications

(20 citation statements)

References 5 publications

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“…The effect of COVID-19 on the short-term load forecasts are analysed in the GB power system. In contrast to mid-and long-term forecast, which make predictions months and years prior to the event, the short-term load forecast have a shorter outlook which range from one hour to weeks before the settlement period [21,22]. Short-term load forecasting plays an important role in scheduling the power plants efficiently in electricity market, as it is essential for economic dispatch and unit commitment [23].…”

Section: Load Forecast Errormentioning

confidence: 99%

“…Short-term load forecasting plays an important role in scheduling the power plants efficiently in electricity market, as it is essential for economic dispatch and unit commitment [23]. As a result, an improved forecast accuracy leads to a more reliable and affordable power system [21].…”

Section: Load Forecast Errormentioning

confidence: 99%

“…This is shown in Figure 10. The forecast error is evaluated by one of the most common performance indicators, namely the mean absolute percentage error (MAPE) [21,23]. MAPE functions well as a forecast performance indicator when employing historical data.…”

Section: Load Forecast Errormentioning

confidence: 99%

See 2 more Smart Citations

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Kirli¹,

Parzen²,

Kiprakis³

2020

Preprint

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The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

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Section: Load Forecast Errormentioning

confidence: 99%

Section: Load Forecast Errormentioning

confidence: 99%

See 1 more Smart Citation

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Kirli¹,

Parzen²,

Kiprakis³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Another combination load forecasting method which imposes in [58] as statistical and physical constraints. In [59], BP-based METLF analysis features such as complexity of parameter, operating speed, and convergence have been proposed. Moreover, the linear, statistical, dynamic, fuzzy logic and AI-based models are similarly used to predict wind speeds.…”

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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“…Although several short term forecasting models have been proposed, overdispersion has not been appropriately handled in these models. The statistical forecasting models such as regression based models and smoothing based models assume that the variance is equal to the mean [4,7,13], or they utilize distributions that ignore the high variations [14,15]. In other models, the high variance in time series is treated to become homogeneous and stationary by Box-Cox and differentiating transformations which increase the computational demand [7].…”

Section: Introductionmentioning

confidence: 99%

Forecasting for smart energy: an accurate and effificient negative binomial additive model

Daraghmi

Daadoo

et al. 2020

IJEECS

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<div>Smart energy requires accurate and effificient short-term electric load forecasting to enable effificient</div><div>energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char</div><div>acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns.</div><div>Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting</div><div>methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel</div><div>model for short-term electric load forecasting. The model adopts the negative binomial additive models</div><div>(NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season</div><div>ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of</div><div>load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM</div><div>captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has</div><div>low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its</div><div>accuracy and effificiency outperform those of the other models used in this context.</div>

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Day ahead hourly load forecast of PJM electricity market and iso new england market by using artificial neural network

Cited by 34 publications

References 5 publications

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

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

Forecasting for smart energy: an accurate and effificient negative binomial additive model

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