{"id":24539656,"url":"https://github.com/codeasarjun/energyvision","last_synced_at":"2025-03-16T04:42:24.992Z","repository":{"id":225618402,"uuid":"766428362","full_name":"codeasarjun/EnergyVision","owner":"codeasarjun","description":"Energyvision","archived":false,"fork":false,"pushed_at":"2024-03-03T08:41:36.000Z","size":1251,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-22T17:15:04.022Z","etag":null,"topics":["arima","arima-forecasting","arima-model","energy-consumption","energy-efficiency","energy-monitor","time-series","time-series-analysis"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/codeasarjun.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-03-03T08:34:11.000Z","updated_at":"2024-08-31T04:51:11.000Z","dependencies_parsed_at":"2024-03-03T09:33:29.324Z","dependency_job_id":"d7c86343-f4e5-4298-a092-cbace9242b51","html_url":"https://github.com/codeasarjun/EnergyVision","commit_stats":null,"previous_names":["codeasarjun/energyvision"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeasarjun%2FEnergyVision","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeasarjun%2FEnergyVision/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeasarjun%2FEnergyVision/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeasarjun%2FEnergyVision/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codeasarjun","download_url":"https://codeload.github.com/codeasarjun/EnergyVision/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243826785,"owners_count":20354220,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["arima","arima-forecasting","arima-model","energy-consumption","energy-efficiency","energy-monitor","time-series","time-series-analysis"],"created_at":"2025-01-22T17:15:12.514Z","updated_at":"2025-03-16T04:42:24.974Z","avatar_url":"https://github.com/codeasarjun.png","language":"Jupyter Notebook","readme":"# 🔍 EnergyVision : Forecasting of Energy Demand\n\n\n\n📊 Objective: The objective of this project is to perform seasonal analysis on a time series dataset of energy demand and develop a forecasting model to predict future energy demand.\n\n🔧 Tools and Libraries Used:\n\n                              Python\n                              Pandas\n                              NumPy\n                              Matplotlib\n                              Statsmodels\u003cbr\u003e\n📈 Data Description:\u003cbr\u003e\nDataset containing historical energy demand data, typically recorded at hourly or daily intervals, along with timestamps. Features: 'Datetime' (timestamp) and 'PJME_MW' (energy demand).\u003cbr\u003e\n\nTarget Variable: 'PJME_MW' (energy demand).\u003cbr\u003e\n\n\n🔍 Exploratory Data Analysis (EDA): \u003cbr\u003e\n\nVisualizing Time Series: visualizing the time series data to observe any trends, seasonality, or irregularities.\u003cbr\u003e\n\n🔄 Seasonality Analysis:\u003cbr\u003e\n\nSeasonal Decomposition: We decomposed the time series into its trend, seasonal, and residual components using the seasonal decomposition of time series (STL) method.\u003cbr\u003e\nSeasonal Subseries Plot: We created a seasonal subseries plot to visualize the seasonal patterns for each month.\u003cbr\u003e\nSeasonal Index: Calculated the seasonal indices to quantify the relative strength of each month's seasonal pattern compared to the overall average.\u003cbr\u003e\n\n📊 Modeling and Forecasting:\u003cbr\u003e\n\nWe split the data into training and test sets, with the training set used to fit the ARIMA model. Model Evaluation: We evaluated the performance of the ARIMA model on the test set using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Forecasting: We utilized the trained ARIMA model to make predictions for future energy demand. \u003cbr\u003e\n\nARIMA Model: We initially fit an ARIMA model to the time series data to capture any non-seasonal patterns and trends.\n\nModel Evaluation: Evaluated the model's performance using metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), and Symmetric Mean Absolute Percentage Error (SMAPE).\n\nForecasting: Generated forecasts for future energy demand using the SARIMA model.\u003cbr\u003e\n\n📉 Results and Insights:\n\nSeasonal analysis revealed strong monthly seasonality in energy demand, with higher demand during certain months of the year.\nWe split the data into training and test sets, with the training set used to fit the ARIMA model. Model Evaluation: We evaluated the performance of the ARIMA model on the test set using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Forecasting: We utilized the trained ARIMA model to make predictions for future energy demand.\n\n🔮 Future Work:\n\nExplore additional features or exogenous variables that may influence energy demand, such as weather data, economic indicators, or special events.\nInvestigate alternative modeling techniques like machine learning models (e.g., LSTM, XGBoost) for forecasting energy demand.\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeasarjun%2Fenergyvision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodeasarjun%2Fenergyvision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeasarjun%2Fenergyvision/lists"}