Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending

Feng, Cong; Zhang, Jie

doi:10.1109/pesgm.2018.8586091

Cited by 27 publications

(10 citation statements)

References 15 publications

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“…It is observed that the load profiles have evident diurnal patterns. This is also proved by a time series analysis that shows all the load time series have the periodicity of 24 (1 day) [21,23]. Moreover, load patterns of the 13 buildings are different, which could be further validated by load statistics shown in Fig.…”

Section: Data Description and Pre-analysismentioning

confidence: 57%

“…Latest MA strategies seek to determine the weights of individual model forecasts dynamically by using AI algorithms, such as multi-model forecasting framework (MMFF) as shown in Fig. 1(b) [20,21]. MMFF is a two-layer machine learning based method for short-term forecasting, which can be described as [17]:…”

Section: Model Aggregation (Ma)mentioning

confidence: 99%

“…The forecasting models (f ij in Fig. 1) adopted are tabulated in Table 1, including four machine learning algorithms with different kernel functions/distribution functions/training algorithms [21]. It is important to note that all these models are used in the first-layer and only one of them is used in the second-layer in MA.…”

Section: M10 Cart Aggregationmentioning

confidence: 99%

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Short-Term Load Forecasting With Different Aggregation Strategies

Feng

Zhang

2018

Volume 2A: 44th Design Automation Conference

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Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects.

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Section: Data Description and Pre-analysismentioning

confidence: 57%

Section: Model Aggregation (Ma)mentioning

confidence: 99%

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Short-Term Load Forecasting With Different Aggregation Strategies

Feng

Zhang

2018

Volume 2A: 44th Design Automation Conference

Self Cite

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“…The time range for the decoder is from time t + 1 to t + T f . Incorporating the information of similar days and hours has been considered in the literature for load forecasting; see e.g., [19], [20]. However, such information is often treated as additional input features or used to generate separate models.…”

Section: B Encoder and Decodermentioning

confidence: 99%

Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach

Xiong¹,

Zhou²,

Chen³

2021

Preprint

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Encoder-decoder-based recurrent neural network (RNN) has made significant progress in sequence-to-sequence learning tasks such as machine translation and conversational models. Recent works have shown the advantage of this type of network in dealing with various time series forecasting tasks. The present paper focuses on the problem of multi-horizon short-term load forecasting, which plays a key role in the power system's planning and operation. Leveraging the encoder-decoder RNN, we develop an attention model to select the relevant features and similar temporal information adaptively. First, input features are assigned with different weights by a feature selection attention layer, while the updated historical features are encoded by a bi-directional long short-term memory (BiLSTM) layer. Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step. Numerical results tested on the dataset of the global energy forecasting competition 2014 show that our proposed model significantly outperforms some existing forecasting schemes.

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“…Benali et al [32] use separate models for the different components of radiation. Similarly, the use of blended learning with a mixture of models has been addressed by several authors [33], [34], [35].…”

Section: Related Workmentioning

confidence: 99%

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

Kraemer

Palma

Bråten

et al. 2020

IEEE Internet Things J.

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For sustainable Internet of Things (IoT) systems, solar-power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large scale IoT systems. We examine which machine learning models, feature sets and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are re-trained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.

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Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending

Cited by 27 publications

References 15 publications

Short-Term Load Forecasting With Different Aggregation Strategies

Short-Term Load Forecasting With Different Aggregation Strategies

Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

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