{"id":"W4294839706","doi":"10.5539/ijsp.v11n5p30","title":"Forecasting Hydropower Generation in Ghana Using ARIMA Models","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Hydropower; Renewable energy; Electricity generation; Production (economics); Electricity; Investment (military); Sustainable development; Operations management; Environmental science; Environmental economics; Business; Agricultural economics; Engineering; Economics; Time series; Mathematics; Power (physics); Statistics; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004640898,0.00007252681,0.0001118388,0.0001134941,0.0000540386,0.00003933258,0.0001102529,0.00001803812,0.00005674062],"category_scores_gemma":[0.00005086824,0.00007510058,0.00002406292,0.00006346609,0.00002079191,0.0001646742,0.00005165004,0.0002094205,7.020694e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963917,"about_ca_system_score_gemma":0.00003675761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004145687,"about_ca_topic_score_gemma":0.00004174307,"domain_scores_codex":[0.9991331,0.00003708277,0.0003881793,0.00007497331,0.0002690865,0.00009757267],"domain_scores_gemma":[0.9996259,0.00006238616,0.0001070117,0.00004082504,0.0001268159,0.00003708844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001922158,0.00002829712,0.001154458,0.00001268745,0.00002996924,0.00004864367,0.0004814061,0.9819351,0.0003646523,0.004604906,0.00007863675,0.011242],"study_design_scores_gemma":[0.0002439826,0.00003990109,0.00008664352,0.00001473188,0.000006175496,0.0001592426,0.00005612634,0.9742553,0.0001134547,0.02473438,0.0002164191,0.00007361353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8427829,0.0002221164,0.1555973,0.0000289817,0.0008119898,0.00003991756,0.00009511432,0.000006064044,0.0004157159],"genre_scores_gemma":[0.9532278,0.00001669264,0.04657059,0.00002138753,0.0001336318,0.000001665283,0.00001440142,0.000009445021,0.000004450566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1104449,"threshold_uncertainty_score":0.3062513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04560892833374964,"score_gpt":0.2584614522356099,"score_spread":0.2128525239018603,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}