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diff --git a/Paper.jl b/Paper.jl new file mode 100644 index 0000000..b1d462f --- /dev/null +++ b/Paper.jl @@ -0,0 +1,3041 @@ +### A Pluto.jl notebook ### +# v1.0.4 + +using Markdown +using InteractiveUtils + +# ╔═╡ 82290cf0-cc7e-11f1-afb5-679eb5954384 +begin + using Downloads + using CodecZlib + using CSV + using DataFrames + using Dates + using GLM + using LinearAlgebra + using Statistics + using JSON + using CairoMakie + using GeoMakie + using FileIO + using ImageIO +end + +# ╔═╡ f6602dd0-292c-4a63-8247-94d626d87f39 +# ╠═╡ disabled = true +#=╠═╡ + + ╠═╡ =# + +# ╔═╡ 57fe870c-3366-4aaf-b2a3-014edf609e7a +md""" +# Ilmatar Energy + +# Alajärvi wind farm complex +""" + +# ╔═╡ 88ef8628-e883-4113-abd3-080e2b2376d8 +md""" +## Weather Stations +- Alajarvi Moksy / Huosianmaa (02787) +""" + +# ╔═╡ 5c21db41-78f6-4db7-ad1e-ff1900afdca3 +md""" +Rather than relying on distant municipal weather stations with historical recording gaps, this analysis leverages high-precision telemetry from the Finnish Meteorological Institute (FMI) automated synoptic station at Alajärvi Möksy (02787), positioned directly at the wind park site. The sample spans January 2021 through October 2026 ($N = 2,078$ days), providing unbroken daily wind records that match the operational aerodynamic roughness of the modern 221 MW complex while offering abundant degrees of freedom for robust regression estimation. +""" + +# ╔═╡ d61dc3ca-fccc-4770-af6d-aa5082c55369 +""" +Fetch daily weather time-series for a given Meteostat station ID. +Meteostat bulk daily files do not contain headers; columns follow the v2 standard schema. +""" +function fetch_meteostat_daily(station_id::String) + url = "https://bulk.meteostat.net/v2/daily/$(station_id).csv.gz" + + # In-memory buffer for the compressed stream + io_buffer = IOBuffer() + Downloads.download(url, io_buffer) + seekstart(io_buffer) + + # Meteostat Daily v2 standard schema + col_names = [ + "date", "tavg", "tmin", "tmax", "prcp", + "snow", "wdir", "wspd", "wpgt", "pres", "tsun" + ] + + # Decompress in-memory and read into DataFrame + decompressed = GzipDecompressorStream(io_buffer) + df = CSV.read( + decompressed, + DataFrame; + header = col_names, + missingstring = ["", "NA", "null"], + types = Dict( + "date" => Date, + "tavg" => Union{Float64, Missing}, + "wspd" => Union{Float64, Missing}, + "pres" => Union{Float64, Missing} + ) + ) + close(decompressed) + + # Meteostat records wspd in km/h; convert to m/s for wind turbine modeling + df.wspd_ms = df.wspd ./ 3.6 + + return df +end + +# ╔═╡ fc5e12f2-3978-4979-935c-0d9c83d8d2e0 +""" +Fetch recent daily data from Meteostat's annual partition endpoint. +Unlike the legacy v2 bulk file, these annual files include a header row. +""" +function fetch_meteostat_year(station_id::String, year::Int) + url = "https://data.meteostat.net/daily/$(year)/$(station_id).csv.gz" + + io = IOBuffer() + try + Downloads.download(url, io) + catch e + @warn "Could not fetch annual data for $year: $e" + return DataFrame() + end + seekstart(io) + + decompressed = GzipDecompressorStream(io) + df = CSV.read(decompressed, DataFrame) + close(decompressed) + + # 1. Reconstruct date from split year/month/day columns + if :year in propertynames(df) && :month in propertynames(df) && :day in propertynames(df) + df.date = Date.(df.year, df.month, df.day) + elseif :date in propertynames(df) + df.date = Date.(string.(df.date)) + else + error("Unrecognized date schema. Available columns: $(names(df))") + end + + if :wspd in propertynames(df) + df.wspd_ms = df.wspd ./ 3.6 + end + + return df +end + +# ╔═╡ faaa0eb8-be12-42a4-abf8-c9ffbb07df9a +function fetch_station_series(station_id::String) + # 1. Fetch legacy archive (up to early 2026) + df_hist = fetch_meteostat_daily(station_id) + + # 2. Fetch 2026 stream + df_2026 = fetch_meteostat_year(station_id, 2026) + + # Standardize columns to :date and :wspd_ms + s_hist = select(df_hist, :date, :wspd_ms) + s_2026 = select(df_2026, :date, :wspd_ms) + + # Combine, deduplicate, and sort + df_all = unique(vcat(s_hist, s_2026), :date) + sort!(df_all, :date) + return df_all +end + +# ╔═╡ 02acee4c-f198-43f5-ab21-939f30969361 +df = fetch_station_series("02787") # 02787 02913 + +# ╔═╡ 4e23c813-6e54-4f47-9345-40d6a698913e +last(df, 5) + +# ╔═╡ 2f2ad881-4177-462c-b2e3-6c2ebc9d8fa1 +let + df_valid = dropmissing(df, :wspd_ms) + println("First non-missing wind record: ", minimum(df_valid.date)) + println("Last non-missing wind record: ", maximum(df_valid.date)) + println("Total days with wind data: ", nrow(df_valid)) +end + +# ╔═╡ 7a3ad16b-4d08-434e-82dd-23f186aa1d9b +let + # 1. Synchronize the start boundary to the first recorded date + start_date = Date(2021, 1, 2) + df_valid = filter(r -> r.date >= start_date && !ismissing(r.wspd_ms), df) + end_date = maximum(df_valid.date) # 2026-10-14 + + # 2. Build continuous calendar grid + all_dates = DataFrame(date = collect(start_date:Day(1):end_date)) + + # 3. Identify missing dates + missing_dates_df = antijoin(all_dates, df_valid, on = :date) + sort!(missing_dates_df, :date) + + total_expected = nrow(all_dates) + total_valid = nrow(df_valid) + total_missing = nrow(missing_dates_df) + completeness = round(total_valid / total_expected * 100, digits = 2) + + println("--- Overview ($start_date to $end_date) ---") + println("Expected Calendar Days: ", total_expected) + println("Recorded Days (Valid): ", total_valid) + println("Total Days Missing: ", total_missing, " ($completeness% complete)\n") + + # 4. Group missing dates into contiguous streaks + if total_missing > 0 + missing_dates = missing_dates_df.date + streaks = DataFrame(start_date = Date[], end_date = Date[], days_lost = Int[]) + + cur_start = missing_dates[1] + cur_prev = missing_dates[1] + + for d in missing_dates[2:end] + if d == cur_prev + Day(1) + cur_prev = d + else + push!(streaks, (cur_start, cur_prev, Dates.value(cur_prev - cur_start) + 1)) + cur_start = d + cur_prev = d + end + end + push!(streaks, (cur_start, cur_prev, Dates.value(cur_prev - cur_start) + 1)) + + # 5. Annual breakdown + missing_dates_df.year = Dates.year.(missing_dates_df.date) + annual_summary = combine(groupby(missing_dates_df, :year), nrow => :missing_days) + + ( + Annual_Missing = annual_summary, + Contiguous_Gaps = sort(streaks, :days_lost, rev = true), + All_Missing_Dates = missing_dates_df.date + ) + else + "No missing days found!" + end +end + +# ╔═╡ 76184222-dd79-4e3b-877a-8eced64fe6ae +df_target_clean = let + # Continuous calendar sequence from the first non-missing day + full_calendar = DataFrame(date = collect(Date(2021, 1, 2):Day(1):Date(2026, 10, 14))) + + # Merge valid records + raw_valid = filter(r -> r.date >= Date(2021, 1, 2) && !ismissing(r.wspd_ms), df) + merged = leftjoin(full_calendar, select(raw_valid, :date, :wspd_ms => :wind_target), on = :date) + + # Linear interpolation across isolated missing days + w = merged.wind_target + for i in 1:length(w) + if ismissing(w[i]) + prev_idx = findlast(!ismissing, w[1:i-1]) + next_offset = findfirst(!ismissing, w[i+1:end]) + next_idx = isnothing(next_offset) ? nothing : i + next_offset + + if !isnothing(prev_idx) && !isnothing(next_idx) + p_val, n_val = w[prev_idx], w[next_idx] + w[i] = p_val + (n_val - p_val) * (i - prev_idx) / (next_idx - prev_idx) + elseif !isnothing(prev_idx) + w[i] = w[prev_idx] + end + end + end + merged.wind_target = Float64.(w) + merged +end + +# ╔═╡ c9c823ad-9875-4efa-82b4-d6189a141b12 +network_master_df = let + predictors = Dict( + # Inner Ring (~30-80 km) + :inner_N => "02725", # Halsua Purola + :inner_E => "02915", # Viitasaari + :inner_S => "02924", # Ähtäri + :inner_W => "02913", # Kauhava + + # Outer Ring (~200-400 km) + :outer_N => "02875", # Oulu + :outer_E => "02917", # Kuopio + :outer_S => "02974", # Helsinki-Vantaa + :outer_W => "02286", # Umeå + ) + + df_net = copy(df_target_clean) + + for (role, st_id) in predictors + st_data = filter(r -> r.date >= Date(2021, 1, 2), fetch_station_series(st_id)) + rename!(st_data, :wspd_ms => Symbol("wind_", role)) + df_net = leftjoin(df_net, select(st_data, :date, Symbol("wind_", role)), on = :date) + end + + # Drop rows where any predictor has missing records to guarantee a balanced regression panel + dropmissing!(df_net) + sort!(df_net, :date) + df_net +end + +# ╔═╡ 3b777682-5faa-47c6-bd70-98a06f368d1a +begin + n_days = nrow(network_master_df) + + model_df = DataFrame( + date = network_master_df.date[3:end], + target = network_master_df.wind_target[3:end], + + # Target Autoregressive memory + lag1_target = network_master_df.wind_target[2:end-1], + lag2_target = network_master_df.wind_target[1:end-2], + + # Inner Ring (t-1 and t-2) + lag1_inner_N = network_master_df.wind_inner_N[2:end-1], + lag2_inner_N = network_master_df.wind_inner_N[1:end-2], + lag1_inner_E = network_master_df.wind_inner_E[2:end-1], + lag2_inner_E = network_master_df.wind_inner_E[1:end-2], + lag1_inner_S = network_master_df.wind_inner_S[2:end-1], + lag2_inner_S = network_master_df.wind_inner_S[1:end-2], + lag1_inner_W = network_master_df.wind_inner_W[2:end-1], + lag2_inner_W = network_master_df.wind_inner_W[1:end-2], + + # Outer Ring (t-1 and t-2) + lag1_outer_N = network_master_df.wind_outer_N[2:end-1], + lag2_outer_N = network_master_df.wind_outer_N[1:end-2], + lag1_outer_E = network_master_df.wind_outer_E[2:end-1], + lag2_outer_E = network_master_df.wind_outer_E[1:end-2], + lag1_outer_S = network_master_df.wind_outer_S[2:end-1], + lag2_outer_S = network_master_df.wind_outer_S[1:end-2], + lag1_outer_W = network_master_df.wind_outer_W[2:end-1], + lag2_outer_W = network_master_df.wind_outer_W[1:end-2] + ) + + const latest_date = maximum(model_df.date) + const split_date = latest_date - Day(365) + + train_df = filter(r -> r.date <= split_date, model_df) + holdout_df = filter(r -> r.date > split_date, model_df) + + println("Training: ", nrow(train_df), " days | Holdout: ", nrow(holdout_df), " days") +end + +# ╔═╡ c126fdcc-e149-4834-af5c-503465ffdede +# fit_model0_and_model1 +begin + # --- Model 0: Baseline Persistence (Y_t = Y_{t-1}) --- + m0_preds = holdout_df.lag1_target + m0_errors = holdout_df.target .- m0_preds + m0_mae = mean(abs.(m0_errors)) + m0_rmse = sqrt(mean(m0_errors.^2)) + + # --- Model 1: Static 4-Direction OLS --- + formula_m1 = @formula(target ~ lag1_target + lag2_target + + lag1_inner_W + lag2_inner_W + + lag1_inner_N + lag2_inner_N + + lag1_inner_E + lag2_inner_E + + lag1_inner_S + lag2_inner_S) + + ols_m1 = lm(formula_m1, train_df) + + m1_preds = predict(ols_m1, holdout_df) + m1_errors = holdout_df.target .- m1_preds + m1_mae = mean(abs.(m1_errors)) + m1_rmse = sqrt(mean(m1_errors.^2)) + + ( + Baseline_M0 = (Holdout_MAE = round(m0_mae, digits=3), Holdout_RMSE = round(m0_rmse, digits=3)), + Static_M1 = (Train_R2 = round(r2(ols_m1), digits=4), Adj_R2 = round(adjr2(ols_m1), digits=4), + Holdout_MAE = round(m1_mae, digits=3), Holdout_RMSE = round(m1_rmse, digits=3)) + ) +end + +# ╔═╡ 7aa0f225-948d-4d1f-8b5a-2262f879783b +# fit_model2_dynamic +begin + # All 18 lagged features (2 target lags + 16 station lags) + feature_symbols = [ + :lag1_target, :lag2_target, + :lag1_inner_N, :lag2_inner_N, :lag1_inner_E, :lag2_inner_E, + :lag1_inner_S, :lag2_inner_S, :lag1_inner_W, :lag2_inner_W, + :lag1_outer_N, :lag2_outer_N, :lag1_outer_E, :lag2_outer_E, + :lag1_outer_S, :lag2_outer_S, :lag1_outer_W, :lag2_outer_W + ] + + X_mat = hcat(ones(nrow(model_df)), Matrix(model_df[:, feature_symbols])) + y_vec = Vector(model_df.target) + + window_len = 365 + n_total = nrow(model_df) + m2_all_preds = fill(NaN, n_total) + beta_history = fill(NaN, n_total, size(X_mat, 2)) + + for t in (window_len + 1):n_total + # Trailing 365-day calibration window + X_w = X_mat[(t - window_len):(t - 1), :] + y_w = y_vec[(t - window_len):(t - 1)] + + # Closed-form OLS solution via QR decomposition + b_t = X_w \ y_w + beta_history[t, :] = b_t + + # 1-day ahead out-of-sample prediction + m2_all_preds[t] = dot(X_mat[t, :], b_t) + end + + model_df.pred_m2 = m2_all_preds + + # Evaluate Model 2 strictly over the exact same holdout period + m2_holdout = filter(r -> r.date in holdout_df.date, model_df) + m2_errors = m2_holdout.target .- m2_holdout.pred_m2 + m2_mae = mean(abs.(m2_errors)) + m2_rmse = sqrt(mean(m2_errors.^2)) + + (Holdout_MAE = round(m2_mae, digits=3), Holdout_RMSE = round(m2_rmse, digits=3)) +end + +# ╔═╡ 4aa84d18-d2ec-4219-b85d-6ba5e08bc7e8 +# inspect_m1_coefficients +coeftable(ols_m1) + +# ╔═╡ 73ed2f4e-38d7-4f64-9ac7-b53acc046200 +# model2_coefficient_summary +let + # All 19 parameter names (Intercept + 18 lagged features) + param_names = vcat( + :Intercept, + [ + :lag1_target, :lag2_target, + :lag1_inner_N, :lag2_inner_N, :lag1_inner_E, :lag2_inner_E, + :lag1_inner_S, :lag2_inner_S, :lag1_inner_W, :lag2_inner_W, + :lag1_outer_N, :lag2_outer_N, :lag1_outer_E, :lag2_outer_E, + :lag1_outer_S, :lag2_outer_S, :lag1_outer_W, :lag2_outer_W + ] + ) + + # Filter only rows where the rolling window had enough history (non-NaN) + valid_mask = .!isnan.(beta_history[:, 1]) + valid_betas = beta_history[valid_mask, :] + + # Calculate distribution metrics across all estimation windows + summary_df = DataFrame( + Parameter = param_names, + Mean_Beta = [round(mean(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)], + Std_Beta = [round(std(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)], + Min_Beta = [round(minimum(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)], + Max_Beta = [round(maximum(valid_betas[:, j]), digits = 4) for j in 1:length(param_names)] + ) + + summary_df +end + +# ╔═╡ 92ea415f-c0d8-4f98-8981-a6680e0999d7 +# model2_latest_snapshot +let + param_names = vcat( + :Intercept, + [ + :lag1_target, :lag2_target, + :lag1_inner_W, :lag2_inner_W, :lag1_inner_N, :lag2_inner_N, + :lag1_inner_E, :lag2_inner_E, :lag1_inner_S, :lag2_inner_S, + :lag1_outer_W, :lag2_outer_W, :lag1_outer_SW, :lag2_outer_SW, + :lag1_outer_N, :lag2_outer_N, :lag1_outer_S, :lag2_outer_S + ] + ) + + latest_betas = beta_history[end, :] + + DataFrame( + Parameter = param_names, + Active_Weight = round.(latest_betas, digits = 4) + ) +end + +# ╔═╡ 399edbd0-61e2-4c72-a3a0-a1147cf868fc +# model2_seasonal_shift +let + valid_mask = .!isnan.(beta_history[:, 1]) + valid_dates = model_df.date[valid_mask] + valid_betas = beta_history[valid_mask, :] + + months = Dates.month.(valid_dates) + winter_mask = (months .== 12) .| (months .== 1) .| (months .== 2) + summer_mask = (months .== 6) .| (months .== 7) .| (months .== 8) + + param_names = vcat( + :Intercept, + [ + :lag1_target, :lag2_target, + :lag1_inner_W, :lag2_inner_W, :lag1_inner_N, :lag2_inner_N, + :lag1_inner_E, :lag2_inner_E, :lag1_inner_S, :lag2_inner_S, + :lag1_outer_W, :lag2_outer_W, :lag1_outer_SW, :lag2_outer_SW, + :lag1_outer_N, :lag2_outer_N, :lag1_outer_S, :lag2_outer_S + ] + ) + + DataFrame( + Parameter = param_names, + Winter_Mean = [round(mean(valid_betas[winter_mask, j]), digits = 4) for j in 1:length(param_names)], + Summer_Mean = [round(mean(valid_betas[summer_mask, j]), digits = 4) for j in 1:length(param_names)], + Seasonal_Δ = [round(mean(valid_betas[winter_mask, j]) - mean(valid_betas[summer_mask, j]), digits = 4) for j in 1:length(param_names)] + ) +end + +# ╔═╡ f0f55d3a-7a07-4e84-bf6a-178150a2b71c +# model_comparison_summary +let + y_true = holdout_df.target + + # 1. Predictions aligned to the exact holdout sample + y_m0 = holdout_df.lag1_target + y_m1 = predict(ols_m1, holdout_df) + + m2_holdout = filter(r -> r.date in holdout_df.date, model_df) + y_m2 = m2_holdout.pred_m2 + + # 2. Metric calculation helper + calc_metrics(y_hat, label) = begin + err = y_true .- y_hat + mae = mean(abs.(err)) + rmse = sqrt(mean(err.^2)) + (Model = label, Holdout_MAE = round(mae, digits=3), Holdout_RMSE = round(rmse, digits=3)) + end + + m0 = calc_metrics(y_m0, "Baseline M0 (Persistence)") + m1 = calc_metrics(y_m1, "Static OLS M1 (Inner Ring)") + m2 = calc_metrics(y_m2, "Dynamic OLS M2 (Rolling 365d)") + + df_comp = DataFrame([m0, m1, m2]) + + # 3. Add percentage gains over persistence baseline + base_mae = df_comp.Holdout_MAE[1] + base_rmse = df_comp.Holdout_RMSE[1] + + df_comp.MAE_Reduction = [ + "-", + "$(round((1 - df_comp.Holdout_MAE[2] / base_mae) * 100, digits=1))%", + "$(round((1 - df_comp.Holdout_MAE[3] / base_mae) * 100, digits=1))%" + ] + df_comp.RMSE_Reduction = [ + "-", + "$(round((1 - df_comp.Holdout_RMSE[2] / base_rmse) * 100, digits=1))%", + "$(round((1 - df_comp.Holdout_RMSE[3] / base_rmse) * 100, digits=1))%" + ] + + df_comp +end + +# ╔═╡ da1115e1-76c3-4c35-a6f3-9714b4263dea +# fetch_station_locations +function fetch_station_metadata(station_ids::Vector{String}) + url = "https://bulk.meteostat.net/v2/stations/lite.json.gz" + + io = IOBuffer() + Downloads.download(url, io) + seekstart(io) + + decompressed = GzipDecompressorStream(io) + raw_stations = JSON.parse(decompressed) + close(decompressed) + + id_set = Set(station_ids) + matched = filter(s -> get(s, "id", "") in id_set, raw_stations) + + rows = map(matched) do s + loc = get(s, "location", Dict()) + + # Robust multilingual name parser (handles "en", "fi", or any available key) + raw_name = get(s, "name", "") + clean_name = if raw_name isa AbstractDict + haskey(raw_name, "en") ? raw_name["en"] : + haskey(raw_name, "fi") ? raw_name["fi"] : + (isempty(raw_name) ? "Unknown" : first(values(raw_name))) + else + string(raw_name) + end + + ( + id = string(s["id"]), + name = clean_name, + country = string(get(s, "country", "")), + lat = Float64(loc["latitude"]), + lon = Float64(loc["longitude"]), + elevation = haskey(loc, "elevation") && !isnothing(loc["elevation"]) ? + Float64(loc["elevation"]) : missing + ) + end + + return DataFrame(rows) +end + +# ╔═╡ b0f27193-5c75-48b5-9bac-044089da7443 +df_coords = begin + target_ids = [ + "02787", # Target: Alajärvi Möksy + "02725", # Inner N: Halsua Purola + "02915", # Inner E: Viitasaari Haapaniemi + "02924", # Inner S: Ähtäri + "02913", # Inner W: Kauhava + "02875", # Outer N: Oulu + "02917", # Outer E: Kuopio + "02974", # Outer S: Helsinki-Vantaa + "02286" # Outer W: Umeå (Sweden) + ] + + coords = fetch_station_metadata(target_ids) + coords.role = map(coords.id) do id + id == "02787" ? :target : + id in ["02725", "02915", "02924", "02913"] ? :inner : :outer + end + sort!(coords, :id) +end + +# ╔═╡ fea4b109-3036-4819-94b4-a0a90626b3fb +# plot_figure1_executive_basemap +begin + # ========================================================================= + # 1. BASEMAP THEME SELECTOR + # Options: :light_gray (Recommended for papers), :topo, :streets, :terrain, :satellite, :osm + # ========================================================================= + basemap_choice = :light_gray + + basemap_endpoints = Dict( + :light_gray => "https://services.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/MapServer/export", + :topo => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Topo_Map/MapServer/export", + :streets => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Street_Map/MapServer/export", + :terrain => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Terrain_Base/MapServer/export", + :satellite => "https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/export", + :osm => "https://services.arcgisonline.com/arcgis/rest/services/OpenStreetMap/MapServer/export" + ) + + # ========================================================================= + # 2. BOUNDING BOX & SQUARE GEOMETRY CALIBRATION + # lon_max extended to 29.6 to provide full margin for Kuopio (27.63°E) + # Coordinates calibrated so that ΔX (Mercator) ≈ ΔY (Mercator) -> 1:1 Square + # ========================================================================= + lon_min, lon_max = 17.6, 29.6 + lat_min, lat_max = 59.7, 65.7 + + lon2x(lon) = lon * 20037508.34 / 180.0 + lat2y(lat) = log(tan((90.0 + lat) * pi / 360.0)) * 20037508.34 / pi + + xmin, xmax = lon2x(lon_min), lon2x(lon_max) + ymin, ymax = lat2y(lat_min), lat2y(lat_max) + + # Square dimension layout + fig_w = 520 + fig_h = 520 + export_scale = 3 # 3x supersampling for publication-grade vector sharpness + srv_w = fig_w * export_scale + srv_h = fig_h * export_scale + + base_url = basemap_endpoints[basemap_choice] + map_url = "$(base_url)?bbox=$(xmin),$(ymin),$(xmax),$(ymax)&bboxSR=3857&imageSR=3857&size=$(srv_w),$(srv_h)&f=image" + + io_map = IOBuffer() + Downloads.download(map_url, io_map) + seekstart(io_map) + basemap_img = rotr90(FileIO.load(io_map)) + close(io_map) + + st_x = lon2x.(df_coords.lon) + st_y = lat2y.(df_coords.lat) + + # ========================================================================= + # 3. FIGURE & AXIS SETUP + # ========================================================================= + fig1 = Figure( + size = (fig_w, fig_h), + fonts = (; + regular = "Inter", + bold = "Inter Bold", + semibold = "Inter SemiBold" + ), + fontsize = 9.5 + ) + + ax = Axis( + fig1[1, 1], + # title = "Ilmatar Alajärvi Wind Farm: Regional Telemetry & Synoptic Arrays", + # titlesize = 11.0, + titlefont = :bold, + aspect = DataAspect() + ) + + hidedecorations!(ax) + hidespines!(ax) + + # Render Basemap Layer + image!(ax, xmin..xmax, ymin..ymax, basemap_img) + + # Coordinates of Target Station (Alajärvi Möksy) + target_idx = findfirst(==( "02787"), df_coords.id) + tx, ty = st_x[target_idx], st_y[target_idx] + + # ========================================================================= + # 4. SYNOPTIC TELEMETRY VECTORS (SPATIAL ARCS) + # ========================================================================= + for i in 1:nrow(df_coords) + if i != target_idx + is_inner = df_coords.role[i] == :inner + # Inner: Cobalt Blue dashed vector | Outer: Deep Rust dashed vector + lines!( + ax, [st_x[i], tx], [st_y[i], ty]; + color = is_inner ? (:royalblue, 0.85) : (colorant"#b45309", 0.75), + linestyle = is_inner ? :dash : :dot, + linewidth = is_inner ? 1.4 : 1.2 + ) + end + end + + inner_mask = df_coords.role .== :inner + outer_mask = df_coords.role .== :outer + + # ========================================================================= + # 5. STATION MARKERS WITH CONTRAST HALOS + # ========================================================================= + # Outer Synoptic Array: Deep Tangerine/Burnt Amber with white outline + scatter!( + ax, st_x[outer_mask], st_y[outer_mask]; + color = "#ea580c", + markersize = 9.5, + strokewidth = 1.4, + strokecolor = :white, + label = "Outer Synoptic Array (~200–400 km)" + ) + + # Inner Ring Array: Deep Royal Blue with white outline + scatter!( + ax, st_x[inner_mask], st_y[inner_mask]; + color = "#1d4ed8", + markersize = 9.5, + strokewidth = 1.4, + strokecolor = :white, + label = "Inner Ring Array (~30–80 km)" + ) + + # Target Asset Marker: Layered Executive Concentric Reticle (Bullseye) + # 1. Outer accent ring + scatter!( + ax, [tx], [ty]; + color = (:crimson, 0.20), + markersize = 22, + strokewidth = 0 + ) + # 2. Main target ring with sharp white border + scatter!( + ax, [tx], [ty]; + color = :crimson, + marker = :circle, + markersize = 13, + strokewidth = 1.8, + strokecolor = :white, + label = "Target Asset: Alajärvi Möksy" + ) + # 3. Inner core center dot + scatter!( + ax, [tx], [ty]; + color = :white, + marker = :circle, + markersize = 4.5, + strokewidth = 0 + ) + + # ========================================================================= + # 6. CARTOGRAPHIC LABELS (Optimized for square extent) + # ========================================================================= + for i in 1:nrow(df_coords) + id = df_coords.id[i] + name = df_coords.name[i] + + offset = offset = if id == "02787" + (7, 7) + elseif id == "02286" # Umeå (Swedish coast) + (-74, -2) + elseif id == "02913" # Kauhava + (-54, 4) + elseif id == "02725" # Halsua Purola + (8, 6) + elseif id == "02924" # Ähtäri + (8, -4) + elseif id == "02915" # Viitasaari + (10, -6) + elseif id == "02917" # Kuopio (Safely inside map margin) + (8, 2) + elseif id == "02875" # Oulu + (8, 5) + elseif id == "02974" # Helsinki-Vantaa + (8, 2) + else + (6, 4) + end + + lbl = id == "02787" ? "Alajärvi Möksy" : name + + text!( + ax, st_x[i], st_y[i]; + text = lbl, + fontsize = id == "02787" ? 9.5 : 8.0, + font = id == "02787" ? :bold : :semibold, + color = "#0f172a", + offset = offset + ) + end + + # Clean executive legend + axislegend( + ax, + position = :lt, + framevisible = true, + backgroundcolor = (:white, 0.92), + framecolor = "#94a3b8", + framewidth = 0.8, + padding = (6, 6, 5, 5), + labelsize = 8.0, + patchsize = (12.0, 8.0) + ) + + # Save vector PDF directly + save("Figure1_Station_Network.pdf", fig1; pt_per_unit = 1.0) + fig1 +end + +# ╔═╡ 3c3ed324-b766-4061-98dc-548a4b71694d +let + slice_mask = Date(2025, 11, 1) .<= model_df.date .<= Date(2025, 12, 5) + df_slice = filter(r -> r.date in model_df.date[slice_mask], model_df) + + holdout_payload = Dict( + "dates" => string.(df_slice.date), + "actual" => round.(df_slice.target, digits = 2), + "m0_pred" => round.(df_slice.lag1_target, digits = 2), + "m1_pred" => round.(predict(ols_m1, df_slice), digits = 2), # Evaluated dynamically from fitted OLS M1 + "m2_pred" => round.(df_slice.pred_m2, digits = 2) # Dynamic 365-day rolling walk-forward + ) + + open("holdout_series.json", "w") do f + JSON.print(f, holdout_payload) + end +end + +# ╔═╡ 9cbf0393-130e-4e6e-a1fc-f0566feec3e3 +# 2. Dynamic Seasonal Parameter Drift (Computed directly from beta_history) +let + valid_mask = .!isnan.(beta_history[:, 1]) + valid_dates = model_df.date[valid_mask] + valid_betas = beta_history[valid_mask, :] + + months = Dates.month.(valid_dates) + winter_mask = (months .== 12) .| (months .== 1) .| (months .== 2) + summer_mask = (months .== 6) .| (months .== 7) .| (months .== 8) + + # Ties directly to feature_symbols to guarantee 1:1 column index alignment with X_mat + param_names = vcat(:Intercept, feature_symbols) + + seasonal_payload = [ + Dict( + "param" => string(param_names[j]), + "winter" => round(mean(valid_betas[winter_mask, j]), digits = 4), + "summer" => round(mean(valid_betas[summer_mask, j]), digits = 4), + "seasonal_del" => round(mean(valid_betas[winter_mask, j]) - mean(valid_betas[summer_mask, j]), digits = 4) + ) + for j in 1:length(param_names) + if param_names[j] != :Intercept # Drops Intercept to focus exclusively on lagged features + ] + + open("seasonal_betas.json", "w") do f + JSON.print(f, seasonal_payload) + end +end + +# ╔═╡ 00000000-0000-0000-0000-000000000001 +PLUTO_PROJECT_TOML_CONTENTS = """ +[deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" +CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" +Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6" +FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" +GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +GeoMakie = "db073c08-6b98-4ee5-b6a4-5efafb3259c6" +ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19" +JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" + +[compat] +CSV = "~0.10.17" +CairoMakie = "=0.15.15" +CodecZlib = "~0.7.9" +DataFrames = "~1.8.2" +FileIO = "~1.20.0" +GLM = "~1.9.5" +GeoMakie = "=0.7.17" +ImageIO = "~0.6.10" +JSON = "~1.11.0" 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╠═f6602dd0-292c-4a63-8247-94d626d87f39 +# ╠═82290cf0-cc7e-11f1-afb5-679eb5954384 +# ╠═57fe870c-3366-4aaf-b2a3-014edf609e7a +# ╠═88ef8628-e883-4113-abd3-080e2b2376d8 +# ╠═5c21db41-78f6-4db7-ad1e-ff1900afdca3 +# ╠═d61dc3ca-fccc-4770-af6d-aa5082c55369 +# ╠═fc5e12f2-3978-4979-935c-0d9c83d8d2e0 +# ╠═faaa0eb8-be12-42a4-abf8-c9ffbb07df9a +# ╠═02acee4c-f198-43f5-ab21-939f30969361 +# ╠═4e23c813-6e54-4f47-9345-40d6a698913e +# ╠═2f2ad881-4177-462c-b2e3-6c2ebc9d8fa1 +# ╠═7a3ad16b-4d08-434e-82dd-23f186aa1d9b +# ╠═76184222-dd79-4e3b-877a-8eced64fe6ae +# ╠═c9c823ad-9875-4efa-82b4-d6189a141b12 +# ╠═3b777682-5faa-47c6-bd70-98a06f368d1a +# ╠═c126fdcc-e149-4834-af5c-503465ffdede +# ╠═7aa0f225-948d-4d1f-8b5a-2262f879783b +# ╠═4aa84d18-d2ec-4219-b85d-6ba5e08bc7e8 +# ╠═73ed2f4e-38d7-4f64-9ac7-b53acc046200 +# ╠═92ea415f-c0d8-4f98-8981-a6680e0999d7 +# ╠═399edbd0-61e2-4c72-a3a0-a1147cf868fc +# ╠═f0f55d3a-7a07-4e84-bf6a-178150a2b71c +# ╠═da1115e1-76c3-4c35-a6f3-9714b4263dea +# ╠═b0f27193-5c75-48b5-9bac-044089da7443 +# ╠═fea4b109-3036-4819-94b4-a0a90626b3fb +# ╠═3c3ed324-b766-4061-98dc-548a4b71694d +# ╠═9cbf0393-130e-4e6e-a1fc-f0566feec3e3 +# ╟─00000000-0000-0000-0000-000000000001 +# ╟─00000000-0000-0000-0000-000000000002 |