Lately, I’ve been working on an implementation of Sequential MonteCarlo (SMC) a.k.a. particle filters, for a probabilistic programming library I’m developing. This family of methods is useful for performing filtering for a wide class of dynamical systems. Filtering corresponds to estimating the current state of a dynamical system given a noisy view of its trajectory until now. An important class of models on which SMC can be used is that of Hidden Markov models. In those, we observe through some possibly noisy function (whence the hidden) the sequence of states of a Markov chain (so the dynamic is itself probabilistic). Kalman filters fall under the same umbrella but focus on continuous state spaces.
In order to better test the library and convince myself that it is useful for something nontrivial, I’ve been looking for an interesting problem to solve using SMC. I’ve settled on forecasting the power output of a wind turbine using the dataset made available here. You can also have a look at the userprovided solutions on the same link for other (perhaps better – I didn’t compare) approaches.
This dataset contains a time series of:
the instantaneous power output of the wind turbine (in kW)
wind speed (in m/s)
as well as some other variables that we will disregard. The recording interval spans over two years and the sampling period is equal to \(10\) minutes. It is known that the wind turbine has a peak output power of \(1750\) kW.
The goal is to construct a model able to predict, in an online fashion, the total power production of the wind turbine over the next 15 days using all data gathered up to now. Note that forecasting is not the same task as filtering: the former consists in estimating future states while the latter, as explained above, seeks to estimate the current state. what we’ll do is construct a dynamical model of power production, solve it on the dataset using filtering and then evaluate the dynamical model forward on the filtered estimate to perform forecasting.
This article follows linearly through the full implementation: take this as a literate programming experiment rather than a blog post, feel free to skip boring code. The full source is available on github.
The plan is as follows.
In Sec. 1 we define some helpers to manipulate time series.
We then load the CSV file containing the data in Sec. 2.
In Sec. 3 we have a shallow look at the data.
In order to illustrate probabilistic programming on a smaller example, we take a small break off our objective and construct in Sec. 4 a simple model predicting power output from wind speed.
The raw data needs to be cleaned and processed a bit before inference. We do that in Sec. 5.
Finally, we build a forecasting model on top of a Kalman filter in Sec. 6.
Depending on the needs, we will represent time series either as values of type (float * 'a) Seq.t
or (int * 'a) Seq.t
(the first component of the sequence denoting time) or use dense representations. We define some convenient functions for manipulating these series.
let sf = Printf.sprintf
(* Naming typical durations *)
let minute = 60.
let hour = minute *. 60.
let day = hour *. 24.
let year = day *. 365.
let month = year /. 12.
(* Compute the (approximate) time derivative of the series *)
let derivative series =
Seq.map2fun (t, x) (t', x') > (t, (x' . x) /. (t' . t)))
(
series
(Seq.tail_exn series)
(* Restrict a signal to a given time interval *)
let restrict low hi =
assert (low < hi) ;
fun (t, _) > t > low && t <= hi)
Seq.filter (
(* If [seq = (t1, x1), (t2, x2), (t3, x3) ...] then
[cumulative seq] is equal to
[(x1, t1), (t2, x1 + x2), (t3, x1 + x2 + x3), ...]. *)
let cumulative ts =
Seq.unfoldfun (prev, s) >
(match s () with
None
 Seq.Nil >
 Seq.Cons ((t, x), s') >let tot = prev +. x in
Some ((t, tot), (tot, s')))
0.0, ts)
(
(* [fold_unfold f state seq] folds [f] over [seq] and constructs
lazily a sequence out of each outcome. *)
let rec fold_unfold f state seq =
match seq () with
 Seq.Nil > Seq.nil
 Seq.Cons (elt, tail) >let (state, elt) = f state elt in
fun () > Seq.Cons (elt, fold_unfold f state tail)
(* Average a [signal] piecewise according to [kernel]. *)
let average (signal : Float.Array.t) (kernel : Float.Array.t) =
let m = Float.Array.length signal in
let n = Float.Array.length kernel in
if n > m then
invalid_arg "average: dimensions mismatch (kernel too big)" ;
(* some samples might be lost but we don't care *)
let s = m / n in
Array.init s (fun i >
Float.let acc = ref 0.0 in
let start = i * n in
for j = 0 to n  1 do
acc :=
!accArray.get signal (start + j)
+. (Float.Array.get kernel j)
*. Float.done ;
!acc)
(* Map the first and second components of a sequence of pairs *)
let map_fst f seq = Seq.map (fun (x, y) > (f x, y)) seq
let map_snd f seq = Seq.map (fun (x, y) > (x, f y)) seq
The data is given as a CSV file, we use the csv library to load it and project out the relevant fields: power and wind speed. Note that the dataset misses a lot of rows: for now we fill those with zeroes but that issue will have to be dealt with later.
(* Loading the CSV file *)
let rows =
Sys.argv.(1) @@ fun ic >
Containers.IO.with_in let csv = Csv.of_channel ic in
(* Correct the header of the CSV *)
let header = Csv.next csv in
true csv ("Date" :: List.tl header) ;
Csv.Rows.set_header ~replace:
Seq.of_list @@ Csv.Rows.input_all csv
(* We select output power and wind speed. Units are not explicited
in the dataset: we posit that wind speed is given in m/s and power
in kilowatts. *)
let proj_attribute row attribute =
match float_of_string (Csv.Row.find row attribute) with
 x > xexception _ > 0.0

let tseries =
let absolute_time =
Seq.fmapfun row >
(match Ptime.of_rfc3339 @@ Csv.Row.find row "Date" with
None
 Error _ >
 Ok (date, _, _) >let seconds = Ptime.to_float_s date in
let power = proj_attribute row "ActivePower" in
let wind_speed = proj_attribute row "WindSpeed" in
Some (seconds, (power, wind_speed)))
rowsin
let (t0, _) = Seq.head_exn absolute_time in
(* Shift to t0 *)
fun (t, data) > (t . t0, data)) absolute_time
Seq.map (
(* The data is supposed to be sampled at 10mn intervals;
we check that it is the case *)
let () =
assert (
Seq.for_all2fun (t, _) (t', _) > t' . t = 10. *. minute)
(
tseries
(Seq.tail_exn tseries))
let sampling_period = 10. *. minute
In order to design our model, we need to understand the process that generated the data. We plot the power and wind speed over the whole second month of recording. We plot as well the total cumulative power over the whole dataset. Here we use the prbnmcngnuplot library to produce the plots.
let power = Seq.map (fun (t, (power, _ws)) > (t, power)) tseries
let wind_speed = Seq.map (fun (t, (_power, ws)) > (t, ws)) tseries
let () =
let open Plot in
let to_days seconds = seconds /. day in
let plot ~xaxis ~yaxis ~title ~legend data =
plot2
~xaxis
~yaxis
~title
[ Line.line_2d
~points:
(datafun (s, p) > Plot.r2 (to_days s) p)
> Seq.map (
> Plot.Data.of_seq)
~legend
() ]in
let wind_speed_plot =
plot"time (days)"
~xaxis:"wind speed (m/s)"
~yaxis:"wind speed"
~title:"wind speed (m/s)"
~legend:1. *. month) (2. *. month))
(wind_speed > restrict (in
let power_plot =
plot"time (days)"
~xaxis:"power (kW)"
~yaxis:"power"
~title:"power (kW)"
~legend:1. *. month) (2. *. month))
(power > restrict (in
let cumulative_power_plot =
plot"time (days)"
~xaxis:"power (kW)"
~yaxis:"cumulative power (full range)"
~title:"cumulative power (kW)"
~legend:
(cumulative power)in
Plot.(
run_matrix
~plots:Some wind_speed_plot ];
[ [ Some power_plot ];
[ Some cumulative_power_plot ]
[
]
~target:(png1024, 512 * 3)
~pixel_size:("plots/raw_data.png"
~png_file:
()) exec)
[fig:raw_data]
Unsurprisingly, wind speed and power seem highly correlated. One observes that the wind speed series seems to be structured as a sequence of peaks with a daily periodicity. One also notes some long periods where wind speed is equal to 0: this correspond to missing rows.
More interestingly, the cumulative power looks piecewise affine with small fluctuations around its mean behaviour. The plot also displays a clear yearly seasonal inflexion.
Let’s look at the power and wind speed distributions over distinct ’seasons’, where ’season’ is understood as a maximal contiguous interval of data that looks affine (ie in what follows I performed the split manually).
(* in days *)
let season_A = ("season_A", (1, 180))
let season_B = ("season_B", (180, 250))
let season_C = ("season_C", (250, 520))
let season_D = ("season_D", (520, 620))
let season_E = ("season_E", (620, 820))
let seasons = [season_A; season_B; season_C; season_D; season_E]
let hist name series =
let points =
snd > Seq.map Plot.r1 > Plot.Data.of_seq in
series > Seq.map 50 ~legend:name ()
Plot.Histogram.hist ~points ~bins:
let season_hist name series =
let open Plot in
let plot (season_name, (a, b)) =
let series = series > restrict (float a *. day) (float b *. day) in
hist season_name seriesin
plot2
~xaxis:name"freq"
~yaxis:"seasonal plot (%s)" name)
~title:(sf List.map plot seasons)
(
> run"plots/%s_seasons.png" name) ())
~target:(png ~png_file:(sf
exec
let () =
let filtered = Seq.filter (fun (_, p) > p > 0.0) wind_speed in
"windspeed" filtered
season_hist
let () =
let filtered = Seq.filter (fun (_, p) > p > 0.0) power in
"power" filtered season_hist
The following plot shows the histograms of wind speed over all seasons. Note that the seasons do not have the same amount of samples and that these histograms are not normalized. We observe that season_B
and season_D
have a higher mean wind speed and have a larger support.
The relationship between wind speed and power output is nonlinear, as will be made explicit in the next section. This manifests very concretely: power output distribution is very skewed towards high values for season_B
and season_D
.
To sum up: power distribution is unimodal on each season. Cumulative power looks piecewise affine, which indicates that power (the first derivative of cumulative power) averaged over sufficiently big windows is close to constant on each season.
Let us take a small break off our objective and look at the relationship between wind speed and power output.
We will produce a model allowing to predict the instantaneous power production as a function of wind speed at any given time. We’ll not use this model in our forecasting model, but it is an interesting application of probabilistic programming to a simple nonlinear regression (with a pretty tame nonlinearity). In practice, we could use this to reduce the original problem to that of forecasting wind speed over a window of time in the future given past observations. Then, one could use dedicated meteorological models to predict wind speed.
We know that the power output is capped to around 1750 kW.
(* Unit: kW *)
let max_power = 1750.
It is known that the power output of a wind turbine is proportional to the cube of the wind speed). (This page contains a nice explanation.) predicted_power
is our model, with a
being the parameter we’d like to fit to the data. (Note that this function is nonlinear because of the min
).
let predicted_power a ws = Float.min max_power (a *. (ws ** 3.))
We prepare an array containing the power and wind speed on a small window. The actual subset of samples we take to perform the inference should not matter, as long as
it is big enough for accurate statistics, but not too big to impair inference speed (or accuracy! more on this latter);
we filter out elements where output power is negative (ie when the wind turbine consumes more energy than it produces – this behaviour is out of scope of our model).
(*  restrict the timeseries to a reasonable subinterval
(big enough for accurate statistics, not too big to not
impair inference speed)
 filter out elements where output power is negative *)
let power_and_wind_speed =
tseries(* restrict to window of interest *)
6. *. month) (6.5 *. month)
> restrict ((* project time out *)
fun (_, data) > data)
> Seq.map ((* filter out samples with p <= 0 *)
fun (p, _) > p > 0.0)
> Seq.filter ((* convert to array for faster iteration *)
Array.of_seq >
Let’s proceed to define a model to fit the scaling parameter a
in predicted_power
. The idea behind the model is pretty simple. We’d like to guess a parameter a
that is such that for all pairs (p, ws)
in power_and_wind_speed
, predicted_power a ws
is close to p
.
We make this formal by scoring each prediction with a gaussian density centered on p
: if the predicted value is spot on for a given pair (p, ws)
, it will correspond to the maximum of the gaussian probability density function. If it is slightly off, the score will be slightly lower. How fast score decreases is specified by the standard deviation of the scoring gaussian, that we set heuristically to max_power/10
. Each pair is scored in this way. The total score associated to a
is the product of all scores over all elements of power_and_wind_speed
(this quantity is computed implicitly by the inference algorithm).
We’ll use Lightweight MetropolisHastings (LMH) inference to fit the model.
open Dagger.Lmh_inference
let model =
let open Infix in
(* We set a reasonably large uniform prior on [a] *)
let* a = sample @@ Stats_dist.float 3.0 in
let+ () =
Array_ops.iterfun (p, ws) >
(let predicted = predicted_power a ws in
(* We score the prediction using a gaussian pdf:
the score will be maximal at the mean.
The bigger the [std], the more lax we are. *)
score @@10.) predicted)
Stats.Pdfs.gaussian ~mean:p ~std:(max_power /.
power_and_wind_speedin
a
Let’s proceed to inference. We drop the first \(10000\) samples for burnin and take the \(10000\) subsequent ones to form the posterior on a
.
let rng_state = Dagger.RNG.make_self_init ()
let samples = stream_samples model rng_state
let samples =
samples10_000 (* burnin *)
> Seq.drop 10_000
> Seq.take List.of_seq >
Let’s plot the posterior:
let () =
let open Plot in
run"plots/scaling_posterior.png" ())
~target:(png ~png_file:
exec
(plot2"a"
~xaxis:"freq"
~yaxis:"Posterior on scaling parameter"
~title:
[ Histogram.histList.map r1 samples))
~points:(Data.of_list (50
~bins: () ])
The posterior is snuggly contained in the \([2.46;2.56]\) interval. For the purposes of this example, it is enough to take the mean of the posterior as our estimate of the scaling parameter.
let inferred_scaling =
(* take the empirical mean *)
List.fold_left ( +. ) 0.0 samples /. 10000.
let () = Format.printf "P(t) = %f x ws(t)^3@." inferred_scaling
The last line above displays something like: "P(t) = 2.513402 x ws(t)^3". Let us define our estimator for the power output as a function of wind speed:
let convert ws = predicted_power inferred_scaling ws
Let’s compare actual vs predicted power output on some interval.
let () =
let series =
4. *. month) ((4. *. month) +. (3. *. day))
tseries > restrict (in
let actual = Seq.map (fun (t, (power, _)) > Plot.r2 t power) series in
let predicted =
Seq.mapfun (t, (_, wind_speed)) > Plot.r2 t (convert wind_speed))
(
seriesin
let open Plot in
run"plots/wind_to_power.png" ())
~target:(png ~png_file:
exec
(plot2"t"
~xaxis:"p"
~yaxis:"Predicted vs actual power output"
~title:"actual" ();
[ Line.line_2d ~points:(Data.of_seq actual) ~legend:"predicted" ()
Line.line_2d ~points:(Data.of_seq predicted) ~legend: ])
Not too bad! We observe a nonlinearity that we didn’t take into account: when wind speed goes under a certain threshold, the wind turbine just doesn’t produce any power. This is normally part of the specification of the turbine, which we don’t have access to. In any case, we won’t need nor use any of that for our forecasting model.
An first issue with the dataset is its many missing rows. We need to fill those out. Another issue is that the sampling period of \(10\) minutes is too finegrained for our purposes. In particular, it features a daily periodic behaviour that is not relevant to our objective. Aggregating data over a longer period has two advantages: it acts as a lowpass filter (removing useless data) and allows to cut on computational resource consumption during filtering.
Let’s implement a solution for the first issue. When loading the CSV, we filled those missing rows with zeroes. We replace consecutive zeroes with the value of a sliding average over a few days worth of data prior to the first zero.
(* This module implements sequence transformers implemented
by folding over the input sequence a function taking
a sliding window as argument. *)
module Windowed_fold = struct
module Queue = CCFQueue
type ('x, 'a) state = { samples : 'x Queue.t; acc : 'a }
let make_transducer f state next_sample =
let (produced, acc) = f state.samples next_sample state.acc in
let (samples, _old_sample) = Queue.take_back_exn state.samples in
let samples = Queue.cons next_sample samples in
({ samples; acc }, produced)
let apply f initial seq =
let transducer = make_transducer f in
fold_unfold transducer initial seq
(* When encoutering a sequence of zeroes, replace by value of
sliding average. *)
let fill_zeroes_with_average window_duration seq =
let nsamples = int_of_float (window_duration /. (10. *. minute)) in
let initial =
Queue.of_list (List.init nsamples (fun _ > 0.0));
{ samples =
acc = `Keep
}in
applyfun samples next_sample acc >
(match (next_sample, acc) with
0.0, `Fill avg) > (avg, acc)
 (
 (_, `Fill _) > (next_sample, `Keep)0.0, `Keep) >
 (let tot = Queue.fold ( +. ) 0.0 samples in
let avg = tot /. float_of_int (Queue.size samples) in
(avg, `Fill avg)
 (x, `Keep) > (x, `Keep))
initial
seqend
Let’s solve the second issue and construct a dataset ready for filtering. We aggregate data on consecutive windows of some fixed duration. Let us call the successive indices of these windows ticks.
We fix the duration of one tick to be one day. We restrict our attention to a subinterval of 300 days for legibility of the plots.
module Data = struct
let start = 1.
let stop = 300.
let tick_duration = day
let ticks_per_day = day /. tick_duration
(* Restrict the time series to the interval of interest *)
let power = restrict (day *. start) (day *. stop) power
(* Coarsen the signal on a perday basis *)
let coarsened_power =
let tick_samples =
int_of_float (tick_duration /. sampling_period) in
(* Fill the missing gaps using a 5day sliding window
estimation of the average *)
let gaps_filled =
snd
power > Seq.map 5. *. day)
> Windowed_fold.fill_zeroes_with_average (Array.of_seq
> Float.in
(* All samples in the averaged interval have equal weight *)
let window = Float.Array.make tick_samples 1.0 in
average gaps_filled window
let tick_to_day i = float_of_int i *. tick_duration /. day
let monotonic = Seq.unfold (fun i > Some (i, i + 1)) 1
(* Coarsened power timeseries with time labelled in ticks *)
let power_ticks =
Array.to_seq coarsened_power)
Seq.zip monotonic (Float.
(* Coarsened power timeseries with time labelled in days *)
let power_days =
Seq.zip
(Seq.map tick_to_day monotonic)Array.to_seq coarsened_power)
(Float.end
Let us now move to our forecasting model. As stated in the introduction, we decompose forecasting as a filtering phase postcomposed by an extrapolation step based on the state estimate. We use an adhoc Kalman filter to estimate the state of the process.
It’s worth building an intuitive idea of how filtering works. The algorithm maintains a set of weighted family of states. A weighted state is called a particle. The weight of a particle corresponds to its conformance to the model. This family is sometimes called a population. At each time step, the population is pushed forward through the dynamical model (which is supposed known, even though it can feature uncertainty of the probabilistic kind). The resulting population is conditioned against the external observations: this is implemented by scoring, i.e. reweighting the particles according to how well they predict the observed data. From that conditioned population, a new population is drawn with a prescribed cardinality (this is the resampling step, which the library does for the user).
We first define a dynamical model and then carry on to defining the forecasting model.
As written above, filtering relies on having a dynamical model of the process. We model power output as a point following Newton’s laws and subject to a random force. The state of the system at time \(t\) is described by three variables:
the power output \(p_t\)
its speed \(\dot p_t\)
its acceleration \(\ddot p_t\)
The dynamics are implemented by discretizing the law of motions using a simple forward Euler step. It is also convenient to make the vector space structure of the state space explicit (we use it when computing averages over a weighted collection of states).
module State : sig
(* The type of states *)
type t
(* [make] creates a state. [power, power', power''] are
the estimates for respectively power, its first and
second derivatives. *)
val make : power:float > power':float > power'':float > t
val power : t > float
val power' : t > float
val power'' : t > float
val zero : t
(* Evaluating dynamics forward *)
val forward : t > span:int > float
val forward_cumulative : t > span:int > float
end = struct
type t = Float.Array.t
let (power, power', power'', dim) = (0, 1, 2, 3)
let make ~power ~power' ~power'' =
Array.map_from_array Fun.id [ power; power'; power'' ]
Float.
let power a = Float.Array.get a power
let power' a = Float.Array.get a power'
let power'' a = Float.Array.get a power''
let zero = Float.Array.make dim 0.0
let forward state_t0 ~span =
let pacc = ref (power state_t0) in
let pacc' = ref (power' state_t0) in
for _i = 1 to span do
pacc := !pacc +. !pacc' ;
pacc' := !pacc' +. power'' state_t0done ;
!pacc
let forward_cumulative (state_t0 : t) ~span =
let pacc = ref (power state_t0) in
let pacc' = ref (power' state_t0) in
let cacc = ref 0.0 in
for _i = 1 to span do
pacc := !pacc +. !pacc' ;
pacc' := !pacc' +. power'' state_t0 ;
cacc := !cacc +. !paccdone ;
!caccend
We perform forecasting over a window of 15 days in the future. Recall that our end goal is the total (ie cumulative) power output over the next 15 days.
(* The maximal power that can be produced in one day. *)
let max_power_per_tick =
let samples = Data.tick_duration /. sampling_period in
samples *. max_power
let forecast_days = 15.
let forecast_span = int_of_float (forecast_days *. Data.ticks_per_day)
open Dagger.Smc_inference
The model takes as arguments:
The estimated state
of the previous tick;
A record_particle
callback;
A sequence of data
, containing the actual power output at each tick.
Each particle evolves its own view of the state. The role of record_particle
is to save the population at each tick: each particle that survives resampling saves its state by calling it. (We could alternatively let each particle carry its trace and gather all traces at the end of the filtering process, but this would not be suitable for online forecasting. To be honest, I’m not totally satisfied by my current approach but using callbacks is typical of concurrent programming hackery, which this is – suggestions of more structured options are welcome).
In the case where the sequence of data is nonempty, the model proceeds in two consecutive steps:
A prediction phase where we evolve the state by one tick according to the dynamics.
An observation step where we condition the evolved state on the observation.
This second phase is the most interesting. First off, we compute the predicted state at the end of the forecast span. We then check that both the predicted power for the current tick and the forecast power are consistent with the physical constraints imposed by the wind turbine. The score_noyield
primitive allows to reweight the particle. (The noyield
suffix indicates that the function does not schedule the particle to be subject to resampling, while the score
primitive does).
We perform two more subsequent conditioning steps. The following line constrains the predicted power to be not too far off the observed one. The smaller the std
, the tighter we force the process to follow the real trajectory. But if we constrain it to much, the trajectory might become incompatible with the constraints imposed by the dynamical model! So we need some slack.
score_noyield15_000. power @@ Stats.Pdfs.gaussian ~mean:observed_power ~std:
The last conditioning has a similar role but for the forecast estimate: we expect that the power cannot drift off too far, too fast during the forecast span.
120_000. forecast) score_noyield (Stats.Pdfs.gaussian ~mean:power ~std:
In both last lines, we tuned the standard deviations by hand, which is bad style. We could imagine letting the inference algorithm discovering the correct stds by itself, by spawning an initial population with random standard deviations and letting successive resampling select out good ones.
The call to yield
schedules the particle for resampling. This acts as a synchronization barrier for all particles (this is due to the structure of the model where the control flow of each particle always goes through this call). After resampling, we record the population using record_particle
.
Finally, we update the particle state by applying a random force to it.
The full code of the model follows:
let rec model (state : State.t) record_particle data =
let open Infix in
match data () with
 Seq.Nil > return ()
 Seq.Cons ((index, observed_power), rest) >(*
predict using forward Euler step
*)
let power' = State.power' state +. State.power'' state in
let power = State.power state +. State.power' state in
(*
observe
*)
let forecast = State.forward state ~span:forecast_span in
let in_bounds x =
if x <= max_power_per_tick && x >= 0.0 then 1.0 else 0.0
in
let* () =
(* reject particles with OOB forecast *)
score_noyield (in_bounds forecast)in
let* () =
(* reject particles with OOB power *)
score_noyield (in_bounds power)in
let* () =
(* condition on the fact that observed power is polluted by
gaussian noise with given std *)
score_noyield15_000. power
@@ Stats.Pdfs.gaussian ~mean:observed_power ~std:in
let* () =
(* condition on the fact that forecast can't be too far off the
current power estmate  the [std] here should be proportional
to [forecast_span], here we guesstimated it empirically *)
120_000. forecast)
score_noyield (Stats.Pdfs.gaussian ~mean:power ~std:in
let* () = yield in
(* Record the resampled population. Note that all particles surviving
a resampling step have equal weight! *)
record_particle ~state ~at_tick:index ;let* power'' = sample @@ Stats_dist.gaussian ~mean:0.0 ~std:500. in
let state = State.make ~power ~power' ~power'' in
model state record_particle rest
Let us run inference. We use 1000 particles: using might reduce variance but it is not really needed for this exposition. Note that it is possible to dynamically fork more particles during the inference; this was not needed either. The process takes less than 5 seconds on my laptop. We get filtered
, a sequence of population of states at each tick.
let filtered =
let nparticles = 1000 in
let rng_state = Dagger.RNG.make_self_init () in
let table = Hashtbl.create 300 in
let record_particle ~state ~at_tick =
Hashtbl.add table at_tick state in
(* There's also an interruptible runner that can be used for online
filtering *)
let _pop =
Non_interruptible.run
nparticles
(model State.zero record_particle Data.power_ticks)
rng_statein
fun (i, _) > (i, Hashtbl.find_all table i)) Data.power_ticks Seq.map (
We need to extract some statistics out of this sequence of populations, typically the mean and quantiles. (We use prbnmcnstats to compute those statistics.)
(* Compute the qth quantile out of a population. *)
let quantile q pop =
let open Stats.Fin.Float in
let mes = counts_of_empirical Stats.float_table pop in
module Basic_structures.Std.Float) mes q
Stats.Fin.Float.quantile (
(* Compute the mean out of a population. *)
let mean pop =
let open Stats.Fin.Float in
let mes =
counts_of_empirical Stats.float_table pop > normalize > as_measurein
mean mes
let filtered statistics =
Seq.mapfun (t, pop) >
(match pop with
 [] > (t, State.zero)
 _ >let pop = Array.of_list pop in
let power = Array.map State.power pop > statistics in
let power' = Array.map State.power' pop > statistics in
let power'' = Array.map State.power'' pop > statistics in
(t, State.make ~power ~power' ~power'')) filtered
Let’s compute and plot the outcome of filtering as well as the forecasted trajectory. In black, we plot the data. In green, we plot the mean filtered trajectory as well as the 0.25 and 0.75th percentiles (being able to quantify uncertainty is one of the chief advantages of bayesian approaches!). In blue, we plot the forecast trajectory and its quantiles. (Feel free to jump over the boring stats and plotting code and access directly Fig. 2.)
let filtered_mean = filtered mean
let filtered_median = filtered median
let filtered_quantile q = filtered (quantile q)
let forecast_by_integration s =
Seq.mapfun (tick, state) >
(let forecast_tick = tick + forecast_span in
let t = Data.tick_to_day forecast_tick in
let forecast_power = State.forward state ~span:forecast_span in
(t, forecast_power))
s
let black = Plot.Style.(default > set_color Plot.Color.black)
let green = Plot.Style.(default > set_color Plot.Color.green)
let blue = Plot.Style.(default > set_color Plot.Color.blue)
let green_squares =
Plot.Style.(
default0.0 0.8 0.0)
> set_color Plot.Color.(rgb 0.4)
> set_point ~ptyp:Plot.Pointtype.square ~psize:
let blue_squares =
Plot.Style.(
default0.0 0.0 0.8)
> set_color Plot.Color.(rgb 0.4)
> set_point ~ptyp:Plot.Pointtype.square ~psize:
let plots =
"data", Data.power_days, black);
[ ("filtmean", filtered_mean > map_snd State.power > to_days, green);
("filt0.25",
( 0.25 > map_snd State.power > to_days,
filtered_quantile
green_squares );"filt0.75",
( 0.75 > map_snd State.power > to_days,
filtered_quantile
green_squares );"forecastint", forecast_by_integration filtered_mean, blue);
("forecastint0.25",
( 0.25 > forecast_by_integration,
filtered_quantile
blue_squares );"forecastint0.75",
( 0.75 > forecast_by_integration,
filtered_quantile
blue_squares ) ]
let () =
let plot (legend, seq, style) =
Plot.Line.line_2d
~points:(Plot.Data.of_seq (seq > Seq.map Plot.tup_r2))
~legend
~style
()in
let prepare_plots timeseries =
let xtics =
let open Plot.Tics in
default0.0 ~incr:5.0
> set_position ~start:90.)
> set_rotate ~degrees:(in
"t" ~yaxis:"kW" ~xtics (List.map plot timeseries)
Plot.plot2 ~xaxis:in
let plot = prepare_plots plots in
Plot.(
run1920, 1080) ~png_file:"plots/forecast.png" ())
~target:(png ~pixel_size:(
exec plot)
[fig:result]
Finally, let’s look at the cumulative power plot. We spare the tired reader the plotting code.
[fig:resultcumulative]
Clearly, it’s not perfect ;)
. It appears the approach has trouble dealing with the seasonal shifts. Since these were not modelled at all, it’s not entirely surprising. We could indeed integrate manually those in the model, sacrificing a bit of generality for more accuracy.
A proper statistical study should carry on and validate the model on some other part of the dataset and perform a rigorous study of its predictive power; we won’t.
I had some fun working on this dataset. I tried out many models before finding something that looked reasonable. I won’t vouch for the quality of the model itself compared to the heavy artillery of LSTMbased models on Kaggle but I’m very satisfied of one fact: it was pretty easy to come up with modifications and iterate fast on the model. Inference performance is not bad, and I think there’s large room for improvement using better data structures for representing particles and especially using OCaml multicore’s fibers instead of my Cps monadbased implementation. In fact it should not be hard to parallelize the inference using a bulksynchronous model.