Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search

Autor: Pavirani, Fabio, Van Gompel, Jonas, Madahi, Seyed Soroush Karimi, Claessens, Bert, Develder, Chris

The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in

Externí odkaz: http://arxiv.org/abs/2411.04011

Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies

Autor: Madahi, Seyed Soroush Karimi, Gokhale, Gargya, Verwee, Marie-Sophie, Claessens, Bert, Develder, Chris

A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement

Externí odkaz: http://arxiv.org/abs/2404.18821

Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees

Autor: Gokhale, Gargya, Claessens, Bert, Develder, Chris

With the ongoing energy transition, demand-side flexibility has become an important aspect of the modern power grid for providing grid support and allowing further integration of sustainable energy sources. Besides traditional sources, the residentia

Externí odkaz: http://arxiv.org/abs/2403.11947

Detailed Report on the Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

Autor: Aguillard, D. P., Albahri, T., Allspach, D., Anisenkov, A., Badgley, K., Baeßler, S., Bailey, I., Bailey, L., Baranov, V. A., Barlas-Yucel, E., Barrett, T., Barzi, E., Bedeschi, F., Berz, M., Bhattacharya, M., Binney, H. P., Bloom, P., Bono, J., Bottalico, E., Bowcock, T., Braun, S., Bressler, M., Cantatore, G., Carey, R. M., Casey, B. C. K., Cauz, D., Chakraborty, R., Chapelain, A., Chappa, S., Charity, S., Chen, C., Cheng, M., Chislett, R., Chu, Z., Chupp, T. E., Claessens, C., Convery, M. E., Corrodi, S., Cotrozzi, L., Crnkovic, J. D., Dabagov, S., Debevec, P. T., Di Falco, S., Di Sciascio, G., Donati, S., Drendel, B., Driutti, A., Duginov, V. N., Eads, M., Edmonds, A., Esquivel, J., Farooq, M., Fatemi, R., Ferrari, C., Fertl, M., Fienberg, A. T., Fioretti, A., Flay, D., Foster, S. B., Friedsam, H., Froemming, N. S., Gabbanini, C., Gaines, I., Galati, M. D., Ganguly, S., Garcia, A., George, J., Gibbons, L. K., Gioiosa, A., Giovanetti, K. L., Girotti, P., Gohn, W., Goodenough, L., Gorringe, T., Grange, J., Grant, S., Gray, F., Haciomeroglu, S., Halewood-Leagas, T., Hampai, D., Han, F., Hempstead, J., Hertzog, D. W., Hesketh, G., Hess, E., Hibbert, A., Hodge, Z., Hong, K. W., Hong, R., Hu, T., Hu, Y., Iacovacci, M., Incagli, M., Kammel, P., Kargiantoulakis, M., Karuza, M., Kaspar, J., Kawall, D., Kelton, L., Keshavarzi, A., Kessler, D. S., Khaw, K. S., Khechadoorian, Z., Khomutov, N. V., Kiburg, B., Kiburg, M., Kim, O., Kinnaird, N., Kraegeloh, E., Krylov, V. A., Kuchinskiy, N. A., Labe, K. R., LaBounty, J., Lancaster, M., Lee, S., Li, B., Li, D., Li, L., Logashenko, I., Campos, A. Lorente, Lu, Z., Lucà, A., Lukicov, G., Lusiani, A., Lyon, A. L., MacCoy, B., Madrak, R., Makino, K., Mastroianni, S., Miller, J. P., Miozzi, S., Mitra, B., Morgan, J. P., Morse, W. M., Mott, J., Nath, A., Ng, J. K., Nguyen, H., Oksuzian, Y., Omarov, Z., Osofsky, R., Park, S., Pauletta, G., Piacentino, G. M., Pilato, R. N., Pitts, K. T., Plaster, B., Počanić, D., Pohlman, N., Polly, C. C., Price, J., Quinn, B., Qureshi, M. U. H., Ramachandran, S., Ramberg, E., Reimann, R., Roberts, B. L., Rubin, D. L., Sakurai, M., Santi, L., Schlesier, C., Schreckenberger, A., Semertzidis, Y. K., Shemyakin, D., Sorbara, M., Stapleton, J., Still, D., Stöckinger, D., Stoughton, C., Stratakis, D., Swanson, H. E., Sweetmore, G., Sweigart, D. A., Syphers, M. J., Tarazona, D. A., Teubner, T., Tewsley-Booth, A. E., Tishchenko, V., Tran, N. H., Turner, W., Valetov, E., Vasilkova, D., Venanzoni, G., Volnykh, V. P., Walton, T., Weisskopf, A., Welty-Rieger, L., Winter, P., Wu, Y., Yu, B., Yucel, M., Zeng, Y., Zhang, C.

We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ pola

Externí odkaz: http://arxiv.org/abs/2402.15410

Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy

Autor: Esfahani, A. Ashtari, Böser, S., Buzinsky, N., Carmona-Benitez, M. C., Cervantes, R., Claessens, C., de Viveiros, L., Fertl, M., Formaggio, J. A., Gaison, J. K., Gladstone, L., Grando, M., Guigue, M., Hartse, J., Heeger, K. M., Huyan, X., Jones, A. M., Kazkaz, K., Li, M., Lindman, A., Marsteller, A., Matthé, C., Mohiuddin, R., Monreal, B., Morrison, E. C., Mueller, R., Nikkel, J. A., Novitski, E., Oblath, N. S., Peña, J. I., Pettus, W., Reimann, R., Robertson, R. G. H., Saldaña, L., Schram, M., Slocum, P. L., Stachurska, J., Sun, Y. -H., Surukuchi, P. T., Telles, A. B., Thomas, F., Thomas, M., Thorne, L. A., Thümmler, T., Tvrznikova, L., Van De Pontseele, W., VanDevender, B. A., Weiss, T. E., Wendler, T., Zayas, E., Ziegler, A.

Publikováno v: Machine Learning: Science and Technology, 5 (2024) 025026

The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external ma

Externí odkaz: http://arxiv.org/abs/2402.13256