A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

Selecting a suitable energy demand forecasting method is challenging due to the complex interplay of long-term trends, short-term seasonalities, and uncertainties. This paper compares four time-series models performance to predict total and peak monthly energy demand in India. Indian's Central Energy Authority's (CEA) existing trend-based model is used as a baseline against (i) Seasonal Auto-Regressive Integrated Moving Average (SARIMA), (ii) Long Short Term Memory Recurrent Neural Network (LSTM RNN) and (iii) Facebook (Fb) Prophet models. Using 108 months of training data to predict 24 months of unseen data, the CEA model performs well in predicting monthly total energy demand with low root-mean square error (RMSE 4.23 GWh) and mean absolute percentage error (MAPE, 3.4%), but significantly under predicts monthly peak energy demand (RMSE 13.31 GW, MAPE 7.2%). In contrast, Fb Prophet performs well for monthly total (RMSE 4.23 GWh, MAPE 3.3%) and peak demand (RMSE 6.51 GW, MAPE 3.01%). SARIMA and LSTM RNN have higher prediction errors than CEA and Fb Prophet. Thus, Fb Prophet is selected to develop future energy forecasts from 2019 to 2024, suggesting that India's annual total and peak energy demand will likely increase at an annual growth rate of 3.9% and 4.5%, respectively.</p

A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

Selecting a suitable energy demand forecasting method is challenging due to the complex interplay of long-term trends, short-term seasonalities, and uncertainties. This paper compares four time-series models performance to predict total and peak monthly energy demand in India. Indian's Central Energy Authority's (CEA) existing trend-based model is used as a baseline against (i) Seasonal Auto-Regressive Integrated Moving Average (SARIMA), (ii) Long Short Term Memory Recurrent Neural Network (LSTM RNN) and (iii) Facebook (Fb) Prophet models. Using 108 months of training data to predict 24 months of unseen data, the CEA model performs well in predicting monthly total energy demand with low root-mean square error (RMSE 4.23 GWh) and mean absolute percentage error (MAPE, 3.4%), but significantly under predicts monthly peak energy demand (RMSE 13.31 GW, MAPE 7.2%). In contrast, Fb Prophet performs well for monthly total (RMSE 4.23 GWh, MAPE 3.3%) and peak demand (RMSE 6.51 GW, MAPE 3.01%). SARIMA and LSTM RNN have higher prediction errors than CEA and Fb Prophet. Thus, Fb Prophet is selected to develop future energy forecasts from 2019 to 2024, suggesting that India's annual total and peak energy demand will likely increase at an annual growth rate of 3.9% and 4.5%, respectively.</p

From random to rational: improving enzyme design through electric fields, second coordination sphere interactions, and conformational dynamics

Enzymes are versatile and efficient biological catalysts that drive numerous cellular processes, motivating the development of enzyme design approaches to tailor catalysts for diverse applications. In this perspective, we investigate the unique properties of natural, evolved, and designed enzymes, recognizing their strengths and shortcomings. We highlight the challenges and limitations of current enzyme design protocols, with a particular focus on their limited consideration of long-range electrostatic and dynamic effects. We then delve deeper into the impact of the protein environment on enzyme catalysis and explore the roles of preorganized electric fields, second coordination sphere interactions, and protein dynamics for enzyme function. Furthermore, we present several case studies illustrating successful enzyme-design efforts incorporating enzyme strategies mentioned above to achieve improved catalytic properties. Finally, we envision the future of enzyme design research, spotlighting the challenges yet to be overcome and the synergy of intrinsic electric fields, second coordination sphere interactions, and conformational dynamics to push the state-of-the-art boundaries

Role of structural dynamics in selectivity and mechanism of non-heme Fe(II) and 2-Oxoglutarate-dependent Oxygenases involved in DNA repair

AlkB and its human homologue AlkBH2 are Fe(II)- and 2-oxoglutarate (2OG)-dependent oxygenases that repair alkylated DNA bases occurring as a consequence of reactions with mutagenic agents. We used molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) methods to investigate how structural dynamics influences the selectivity and mechanisms of the AlkB- and AlkBH2-catalyzed demethylation of 3-methylcytosine (m3C) in single (ssDNA) and double (dsDNA) stranded DNA. Dynamics studies reveal the importance of the flexibility in both the protein and DNA components in determining the preferences of AlkB for ssDNA and of AlkBH2 for dsDNA. Correlated motions, including of a hydrophobic β-hairpin, are involved in substrate binding in AlkBH2–dsDNA. The calculations reveal that 2OG rearrangement prior to binding of dioxygen to the active site Fe is preferred over a ferryl rearrangement to form a catalytically productive Fe(IV)═O intermediate. Hydrogen atom transfer proceeds via a σ-channel in AlkBH2–dsDNA and AlkB–dsDNA; in AlkB–ssDNA, there is a competition between σ- and π-channels, implying that the nature of the complexed DNA has potential to alter molecular orbital interactions during the substrate oxidation. Our results reveal the importance of the overall protein–DNA complex in determining selectivity and how the nature of the substrate impacts the mechanism

Dioxygen binding is controlled by the protein environment in non-heme FeII and 2-oxoglutarate oxygenases: a study on histone demethylase PHF8 and an ethylene-forming enzyme

Invited for the cover of this issue are Christo Z. Christov and co-workers at Michigan Technological University, University of Oxford, and Michigan State University. The image depicts the oxygen diffusion channel in class 7 histone demethylase (PHF8) and ethylene-forming enzyme (EFE) and changes in the enzymes’ conformations upon binding. Read the full text of the article at 10.1002/chem.202300138

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

MULTILEVEL COMPUTATIONAL INVESTIGATION INTO THE DYNAMICS AND REACTION MECHANISMS OF NON-HEME IRON AND 2-OXOGLUTARATE DEPENDENT ENZYMES

Computational chemistry methods have been extensively applied to investigate biological systems. This dissertation utilizes a multilevel computational approach to explore the dynamics and reaction mechanisms of two groups of enzymes belonging to non-heme Fe(II) and 2-oxoglutarate (2OG) dependent superfamily – histone lysine demethylases from class 7 and ethylene forming enzyme (EFE). Chapter 2 uncovers the role of conformational dynamics in the substrate selectivity of histone lysine demethylases 7A and 7B. The molecular dynamics (MD) simulations of the two enzymes revealed the importance of linker flexibility and dynamics in relative orientations of the reader (PHD) and the catalytic (JmjC) domains. Chapter 3 describes the use of combined quantum mechanics/molecular mechanics (QM/MM) and MD simulations to explore the reaction mechanism of histone lysine demethylases 7B (PHF8), including dioxygen activation, 2OG binding modes, and substrate demethylation steps. Importantly, the calculations imply the rearrangement of the 2OG C-1 carboxylate prior to dioxygen binding at a five-coordination stage in catalysis, highlighting the dynamic nature of the non-heme Fe-center. Chapter 4 develops a computational framework for identifying second coordination sphere (SCS) and especially long range (LR) residues relevant for catalysis through dynamic cross correlation analysis (DCCA) using the PHF8 as a model oxygenase and explores their effects on the rate determining hydrogen atom transfer step. The results from the QM/MM calculations suggest that DCCA can identify non-active site residues relevant to catalysis. Chapter 5 explores the unique catalytic mechanism of EFE. In particular, the study elucidates the atomic and electronic structure determinants that distinguish between ethylene formation and L-Arg hydroxylation reaction mechanisms in the EFE. The results indicated that synergy between the conformation of L-Arg and the coordination mode of 2OG directs the reaction toward ethylene formation or L-Arg hydroxylation. Chapter 6 demonstrates that applying an external electric field (EEF) along the Fe-O bond in the EFE·Fe(III)·OO.-·2OG·L-Arg complex can switch the EFE reactivity between L-Arg hydroxylation and ethylene generation. Overall, applying an EEF on EFE indicates that making the intrinsic electric field of EFE less negative and stabilizing the off-line binding of 2OG might increase ethylene generation while reducing L-Arg hydroxylation. Chapter 7 probes the role of the protein environment in modulating the dioxygen diffusion and binding and thus ultimately contributing to the diverging reactivities of PHF8 and EFE. Overall, the results of this dissertation together highlight the several catalytic strategies utilized by the non-heme Fe(II) and 2OG dependent enzymes for achieving their reaction outcomes. In the longer term, the results can be used to modulate the activities of these enzymes either through enzyme redesign or the generation of enzyme-selective inhibitors