Load forecasting for diurnal management of community battery systems

Wolfs, P.; Emami, Kianoush; Lin, Yuping; Palmer, Edward

doi:10.1007/s40565-018-0392-6

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…A sensitivity analysis was carried out to ascertain the price volatility required to generate profit from energy arbitrage operations (Metz and Tomé 2018). A community LV distribution system equipped with ANFIS was used for voltage management, energy arbitrage and peak load reduction, respectively (Wolfs et al 2018).…”

Section: Energy Arbitragementioning

confidence: 99%

A comprehensive review of demand side management in distributed grids based on real estate perspectives

Dahiru

Daud

Tan

et al. 2023

Environ Sci Pollut Res

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A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the resources and customer demand uncertainties that are attributed to climates. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to system operations. However, demand side management is preferred, as it allows more options for customer participation and active management of energy in buildings. Moreover, the demand side management can simply follow supplies. This paper investigates the implications of demand side management as it affects planning and operations in renewable energy-based distributed grids. Integration of demand side management in customer-oriented plans such as the time-of-use and real-time-pricing on residential and commercial demands is conceptualised to ensure effective customer participation which maintains the valued comforts. Moreover, the optimised tariff integrated demand side management implementations based on the utility-initiated demand response programmes are envisaged to offset conflicting objectives of the economy and customer comforts within residential and commercial demands and are also viewed as a step towards efficient management of energy in buildings.

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Section: Energy Arbitragementioning

confidence: 99%

A comprehensive review of demand side management in distributed grids based on real estate perspectives

Dahiru

Daud

Tan

et al. 2023

Environ Sci Pollut Res

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“…The structure in Fig. 4 shows that the WNN consists of an input layer, a hidden layer (wavelet layer), and an output layer [21]. The WNN combines artificial neural network (ANN) [22] and wavelet analysis.…”

Section: A Wnnmentioning

confidence: 99%

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

Yan

et al. 2021

Journal of Modern Power Systems and Clean Energy

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To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper innovatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for individual optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to modify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network (ANN), WNN, and PSO-WNN.

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“…In the residential areas of Australia, the peak electricity load demand is usually during the afternoons. Surprisingly, the residential afternoon load peak may even be higher than the midday commercial and industrial electricity load demand [1]. To meet such unexpected electricity demands in different sectors and supply the required load, it is necessary to forecast the minimal demand in advance.…”

Section: Introductionmentioning

confidence: 99%

“…Load forecasting techniques are the most widely used solutions to meet the demand in power supply. Electricity supplier's uses past consumption data that can characterize the load forecasting for the estimation of future power utilization to satisfy the future supply and demand [1]. Electricity suppliers face numerous challenges in providing secure, cost-effective and adequate load capacity to the clients in their shelter.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, with these fluctuating energy demands, the present need to devise a legitimate energy creation and supply procedure, decrease control wastage and provide sufficient power to the energy grid is of great importance. It is, therefore, increasingly essential to predict future loads on a regular hourly schedule by utilizing short-term electric load forecasting [1]. The short-term load forecasting period is usually every 24 or 48 hours or even fortnightly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network

Srinivasan¹,

Balasubramanian²,

Selvaraj³

2021

IJETT

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Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for Short-Term Electrical Load Forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m 2 ) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularization (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand.

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Load forecasting for diurnal management of community battery systems

Cited by 8 publications

References 18 publications

A comprehensive review of demand side management in distributed grids based on real estate perspectives

A comprehensive review of demand side management in distributed grids based on real estate perspectives

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network

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