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1. Ensemble Methods for Probabilistic Solar Power Forecasting: A Comparative Study

   https://doi.org/10.1109/PESGM52003.2023.10253133  

   Ahmad, Tawsif; Zhou, Ning (July 2023, IEEE)

   To guide the selection of probabilistic solar power forecasting methods for day-ahead power grid operations, the performance of four methods, i.e., Bayesian model averaging (BMA), Analog ensemble (AnEn), ensemble learning method (ELM), and persistence ensemble (PerEn) is compared in this paper. A real-world hourly solar generation dataset from a rooftop solar plant is used to train and validate the methods under clear, partially cloudy, and overcast weather conditions. Comparisons have been made on a one-year testing set using popular performance metrics for probabilistic forecasts. It is found that the ELM method outperforms other methods by offering better reliability, higher resolution, and narrower prediction interval width under all weather conditions with a slight compromise in accuracy. The BMA method performs well under overcast and partially cloudy weather conditions, although it is outperformed by the ELM method under clear conditions. 

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2. Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks

   Pinceti, A.; Sankar, L.; Kosut, O (January 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   null (Ed.)

   A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications. 

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3. Experimental evaluation of power distribution to reactive loads in a network-controlled delivery grid

   https://doi.org/10.1109/FMEC.2018.8364065  

   Jiang, Zhengqi; Shah, Haard; Rojas-Cessa, Roberto; Grebel, Haim; Mohamed, Ahmed (April 2018, Conference Paper published Apr 2018 in 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC))

   We present experiments with combined reactive and resistive loads on a testbed based on the Controlled-Delivery power Grid (CDG) concept. The CDG is a novel data-based paradigm for distribution of energy in smart cities and smart buildings. This approach to the power grid distributes controlled amounts of power of loads following a request-grant protocol performed through a parallel data network. This network is used as a data plane that notifies the energy supplier about requests and inform loads of the amount of granted power. The energy supplier decides the load, amount, and the time power is granted. Each load is associated with a network address, which is used at the time when power is requested and granted. In this way, power is only delivered to selected loads. Knowing the amount of power being supplied in the CDG requires knowing the precise amount of power demand before this is requested. While the concept works well for an array of resistive loads, it is unclear how to apply it to reactive loads, such as motors, whose power consumption varies over time. Therefore, in this paper, we implement a testbed with multiple loads, two light bulbs as resistive loads and an electrical motor as a reactive load. We then propose to use power profiles for the adoption of the request-grant protocol in the CDG concept. We adopt the use of power profiles to leverage the generation of power requests and evaluate the efficiency of the request-grant protocol on the amount of supplied power. In addition, the deviation of delivered power in the data and power planes is evaluated and results show that the digitized power profile of the reactive loads enables the issuing of power requests for such loads with high accuracy. 

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4. Hardware-Friendly Two-Stage Spatial Equalization for All-Digital mmWave Massive MU-MIMO

   https://doi.org/10.1109/IEEECONF51394.2020.9443327  

   Castaneda, Oscar; Jacobsson, Sven; Durisi, Giuseppe; Goldstein, Tom; Studer, Christoph (November 2020, 54th Asilomar Conference on Signals, Systems, and Computers)

   Next generation wireless communication systems are expected to combine millimeter-wave communication with massive multi-user multiple-input multiple-output technology. All-digital base-station implementations for such systems need to process high-dimensional data at extremely high rates, which results in excessively high power consumption. In this paper, we propose two-stage spatial equalizers that first reduce the problem dimension by means of a hardware-friendly, low-resolution linear transform followed by spatial equalization on a lower-dimensional signal. We consider adaptive and non-adaptive dimensionality reduction strategies and demonstrate that the proposed two-stage spatial equalizers are able to approach the performance of conventional linear spatial equalizers that directly operate on high-dimensional data, while offering the potential to reduce the power consumption of spatial equalization. 

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5. Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification

   https://doi.org/10.3390/en14051481  

   Roth, Jonathan; Chadalawada, Jayashree; Jain, Rishee K.; Miller, Clayton (March 2021, Energies)

   null (Ed.)

   As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use. 

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