Deep Learning Based Forecasting of Individual Residential Loads Using Recurrence Plots

Rajabi, Roozbeh; Estebsari, Abouzar

doi:10.1109/ptc.2019.8810899

Cited by 51 publications

(24 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As more advanced solutions, demand side management is proposed to support accommodation of more RES in distribution networks [18], [19], [20]. Different Demand Response (DR) methods are in place to provide flexibility.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Estebsari

Patti

Bottaccioli

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

Nowadays, integration of more renewable energy resources into distribution systems to inject more clean energy introduces new challenges to power system planning and operation. The intermittent behaviour of variable renewbale resources such as wind and PV generation would make the energy balancing more difficult, as current forecasting tools and existing storage units are insufficient. Transmission system operators may withstand some level of power imbalance, but fluctuations and noise of profiles are undesired. This requires local management performed or encouraged by distribution system operators. They could try to involve aggregators to exploit flexibility of loads through demand response schemes. In this paper, we present an optimal power flow-based algorithm written in Python which reads flexibility of different loads offered by the aggregators from one side, and the power flow deviation with respect to the scheduled profile at transmission-distribution coupling point from the other side, to define where and how much load to adjust. To demonstrate the applicability of this core, we setup a realtime simulation-based test bed and realised the performance of this approach in a real-like environment using real data of a network.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Estebsari

Patti

Bottaccioli

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

show abstract

“…Deep Learning networks are strong methods in the field of machine learning that can solve either classification or regression problems [27]. Deep networks consist of an input layer and an output layer and several hidden layers.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

et al. 2020

Self Cite

View full text Add to dashboard Cite

The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.

show abstract

“…As deep networks have a high capability to recognize and digest images [42], if time series are encoded as images, the performance of the classification and forecasting of them using deep networks would be improved. There are several methods to encode time series into images, including recurrence plots (RP) [43], the Gramian angular field (GAF), and the Markov transition field (MTF) [44]. In the following subsections, these methods are investigated and applied to encode individual household power consumption data as images.…”

Section: Time Series Image Codingmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

Self Cite

View full text Add to dashboard Cite

The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

show abstract

Deep Learning Based Forecasting of Individual Residential Loads Using Recurrence Plots

Cited by 51 publications

References 22 publications

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Contact Info

Product

Resources

About