Decision Support Application for Energy Consumption Forecasting

Jozi, Aria; Pinto, Tiago; Praça, Isabel; Vale, Zita

doi:10.3390/app9040699

Cited by 11 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This appeals to every profit-making business, more especially as the cost of electricity in South Africa (SA) continues to rise [4]. It is therefore important to develop modelling frameworks which can be used in any prediction conditions as proposed by Reference [5].…”

Section: Introductionmentioning

confidence: 99%

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

et al. 2019

View full text Add to dashboard Cite

Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.

show abstract

Section: Introductionmentioning

confidence: 99%

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To provide the final rules, all the created rules will be divided into different groups and the rule with the highest importance degree is chosen in each group [16].…”

Section: ) Create a Combined Fuzzy Rule Basementioning

confidence: 99%

Forecasting Refrigerators Consumption to Support their Aggregated Participation in Demand Response

Faria

Jozi

Vale

2020

2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Syst

Self Cite

View full text Add to dashboard Cite

Demand response programs have become very relevant. However, one of the important facts to have a reliable DR program is the creation of a clear and trustable perspective of the load consumption during the upcoming time periods. On another hand, the increment of the energy-based systems and different energy consuming appliances in the last decades results in larger daily energy consumption which creates the unpredictability of the energy demand. This variety of consumption profiles requires not only consideration of the total consumption of each consumer, but more detailed and focused studies on each type of energy consuming devices. Therefore, this paper proposes a system containing a combination of different forecasting and clustering algorithms to predict the power consumption of several refrigerators and aggregate them into certain groups based on the characteristics of their consumption profiles. Sequentially, the obtained results will be aggregated and serve as basis in order to schedule refrigerators in the context of a demand response program. In the case-study, 20000 refrigerators are considered.

show abstract

“…Each time that user update the forecast can perform a new optimization. Regarding the influence of the forecasting results on optimization, in the case that the presented day-ahead forecasting strategies in References [30,31] are considered, the forecasting error, using Supporter Vector Machine algorithms to predict the values for the next 24 h, will be 9.11%. Figure 3 with different colors.…”

Section: Case Studymentioning

confidence: 99%

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

et al. 2019

Self Cite

View full text Add to dashboard Cite

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.

show abstract

Decision Support Application for Energy Consumption Forecasting

Cited by 11 publications

References 35 publications

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Forecasting Refrigerators Consumption to Support their Aggregated Participation in Demand Response

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

Contact Info

Product

Resources

About