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German Electricity Load Forecasting Using Time Series and Machine Learning Models
This individual assessment focuses on analysing and forecasting German electricity load using time-series analysis, statistical forecasting models and machine-learning approaches. Students are required to work with publicly available electricity data from the Open Power System Data platform and investigate the characteristics of the German time series before developing and comparing a range of forecasting models. The project combines exploratory data analysis, time-series modelling, regression-based forecasting and neural-network modelling, with particular emphasis on critical evaluation of modelling choices and forecast performance. The first stage requires students to retrieve the 60-minute electricity data and isolate the German data using the country identifier “DE”. The data should be aggregated into weekly and daily values using tools such as Pandas, with observations retained from 1 January 2015 through October 2020. Students must produce initial plots and conduct exploratory data analysis to identify the main components of the time series, including possible trend and seasonal patterns. Time-series analysis should then be performed to investigate and test for non-stationarity. The modelling component begins with benchmark forecasting methods such as Mean, Naive, Seasonal Naive and Drift forecasts. Students are required to use a two-year forecast horizon and compare the performance of these benchmark approaches. An autoregressive modelling approach must then be developed using SARIMA where appropriate. Students must identify suitable model parameters using the AIC likelihood method, considering the required combinations of p, d, q, P, D and Q parameters. Model fit should be assessed through residual analysis, including inspection of residual distributions and autocorrelation plots. Forecasts should include confidence intervals and performance should be evaluated using suitable metrics such as RMSE. The assessment also requires an exogenous temperature variable to be incorporated into a SARIMAX model. Berlin is specified as the representative location for German temperature data. Students must recognise the distinction between a true operational forecast and an explanatory or conditional forecast when future observed temperature values are used. The temperature variable should also be used to produce weekly forecasts. Further modelling requires students to apply a feature-based regression model, such as Random Forest or Gradient Boosting Regression, to the weekly electricity-load and temperature data. The final modelling stage uses hourly data with a Long Short-Term Memory (LSTM) neural network. Students must conduct a literature review relating to LSTM applications for the forecasting task, build and tune the model, evaluate its performance and forecast the final two years of available data. The report must critically compare the models rather than simply present numerical results. Students must address specific questions concerning improvement over the Seasonal Naive benchmark, prevention of data leakage when constructing temperature lag features, SARIMAX parameter choices, the value and availability of temperature and holiday covariates, and the relative interpretability and complexity of SARIMAX, feature-based and neural-network models. Students must also recommend one model for operational use based on accuracy, uncertainty, interpretability and maintenance considerations. The final report should be 6–8 pages and include model forecast plots, an evaluation-metrics comparison table, critical discussion of the results, comparison with real data, future improvements and appropriate references. Students must also create a GitHub repository containing the codebase and follow appropriate coding practices. The submitted code must reproduce the figures, models and numerical values presented in the report.
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Programming for Data Science
4,000 words
Programming for Data Science – Individual Portfolio
This individual portfolio assessment for the Programming for Data Science module at Coventry University consists of four tasks designed to assess programming, debugging, data science, data visualisation, data protection and data ethics skills. The assessment carries 20 credits and has a total value of 4,000 words equivalent, excluding the reference list and output. Students are required to submit one clearly organised report containing all four tasks, with each task beginning on a new page. Python code, outputs and relevant plots must be included directly within the report. Task 1 focuses on analysing, critiquing and debugging Python code. Students are required to identify syntax errors, logical errors, style and readability issues in a supplied program, determine what the program is intended to calculate, and make appropriate corrections. Students must test the program using varying values, explain the changes made, and improve its overall readability and annotation so that an unfamiliar user can understand it. The task also requires students to investigate computational efficiency by measuring execution time for different input limits and identifying more efficient coding or logical approaches. Task 2 requires students to design, build and test a simple Python implementation of the Blackjack card game. The program should simulate a single-player game against a computer-controlled dealer, allow the player to choose between hitting and standing, automatically simulate the dealer's turn, and offer the option to play additional rounds. Complex rules such as splitting and betting are excluded, and students must not implement a Python class or graphical visualisation. The submission must include the Python code and the output from five games, with sufficient storytelling in the output to allow the code to be tested from the results shown. Task 3 assesses the student's ability to critically assess, select and apply data science tools. Students work with animal lifespan information from the AnAge database and use Python, including pandas and appropriate graphical libraries, to explore and communicate insights. The task involves summarising the number of animal species represented within each animal Class and producing plots of maximum longevity against adult weight for the four Classes with the most represented species. Students must discuss whether smaller or larger animals live longer, identify extreme outliers, compare trends between animal groups and consider implications for ageing research. Task 4 examines data protection and data ethics using the Cancer Genome Atlas (TCGA) as a case study. Students must explain how a potential data breach could occur, identify the personal and sensitive information that could be compromised, and discuss consequences for patient confidentiality, institutional reputation, participation in future research and possible legal or public relations responses. A second part considers a hypothetical UK database and requires discussion of GDPR and the UK Government Data Ethics Framework, including informed consent, anonymisation, transparency, ethical governance, privacy and public trust in biomedical research. The assessment assesses two module learning outcomes. MLO2 focuses on designing, building, testing, adapting and critiquing small programs in a high-level programming language and is assessed through Tasks 1 and 2. MLO3 focuses on critically assessing, selecting and applying data science tools, libraries or algorithms throughout the data science project lifecycle and is assessed through Tasks 3 and 4. The assignment requires APA referencing and asks students to provide in-text citations and reference lists where relevant. The brief also classifies the assessment as “Amber” for Generative AI: AI tools may be used for inspiration but not to generate answers or analyse datasets. Any permitted use must be clearly acknowledged, documented and cited using APA style.
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Machine Learning / Data Science
Linear Regression and Stability of the Moore–Penrose Pseudoinverse Using Python
This machine learning practical and assessment activity develops an understanding of linear regression, Ordinary Least Squares and the Moore–Penrose pseudoinverse using Python. The work progresses from generating synthetic regression datasets to implementing regression algorithms manually, applying established machine learning libraries, analysing real datasets and evaluating the stability of estimated regression coefficients. The laboratory component begins with the generation of synthetic linear regression data using NumPy, including explanatory variables, random noise and an outcome variable. Students then implement simple linear regression without relying on machine learning libraries, using the least-squares solution to estimate the intercept and slope. The resulting observations and fitted regression line are visualised using Matplotlib. The work is subsequently extended to multiple linear regression, where several independent variables are used and coefficients are first calculated manually before the same problem is solved using Scikit-learn. The laboratory also introduces application of regression to the Scikit-learn Diabetes dataset, including feature and target standardisation, model fitting, prediction, correlation analysis and interpretation of regression coefficients. It also highlights the importance of residual analysis when assessing whether a linear model is appropriate. The associated weekly challenge focuses on the stability of linear regression solutions estimated using the Moore–Penrose pseudoinverse. Using a house-price dataset containing variables such as property size, number of bedrooms, distance from the city centre and property age, students construct the design matrix, standardise features and the response variable, and calculate regression coefficients using the pseudoinverse. Students then investigate model robustness by repeatedly fitting the regression model to random subsamples of different sizes and analysing the mean and standard deviation of each coefficient. Tables, boxplots or error-bar visualisations can be used to compare coefficient variability. The final discussion considers which variables are most influential, which coefficients are most stable, how sample size affects stability and whether coefficient interpretation remains reliable across different samples. The final work is submitted as a single PDF exported from Jupyter Notebook or Google Colab, combining documented Python code, experimental results, plots and written interpretation in a professionally organised notebook. Overview word count: approximately 360 wor
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