title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ð° Ð¸ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÐ½ÐµÑÐ³Ð¸Ð¸, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ðµ Ð½Ð° Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Deep forecasting of the energy production and consumption
Creators: name: ÐÐµÐ½Ð½Ð¸, ÐÐ¸ÑÐ°ÑÐ»Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸
Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ð° ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸
ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð´Ð¾Ð»Ð³Ð°Ñ ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð°Ñ Ð¿Ð°Ð¼ÑÑÑ
deep forecasting
time series analysis
electricity production
electricity consumption
recurrent neural network
long short-term memory

description: Ð 2016 Ð³Ð¾Ð´Ñ Ð½Ð° Ð´Ð¾Ð»Ñ Ð·Ð´Ð°Ð½Ð¸Ð¹ Ð¿ÑÐ¸ÑÐ¾Ð´Ð¸Ð»Ð¾ÑÑ 41% ÐºÐ¾Ð½ÐµÑÐ½Ð¾Ð³Ð¾ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð² ÐÐ²ÑÐ¾Ð¿Ðµ. ÐÐ¾ÑÑÐ¾Ð¼Ñ ÐºÑÐ°Ð¹Ð½Ðµ Ð²Ð°Ð¶Ð½Ð¾ Ð¿Ð¾Ð²ÑÑÐ¸ÑÑ Ð¸Ñ ÑÐ½ÐµÑÐ³Ð¾ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ. ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð² Ð·Ð´Ð°Ð½Ð¸ÑÑ Ð¿Ð¾Ð¼Ð¾Ð¶ÐµÑ Ð¿Ð¾ÑÑÐµÐ±Ð¸ÑÐµÐ»ÑÐ¼ ÑÐºÐ¾Ð½Ð¾Ð¼Ð¸ÑÑ ÑÐ½ÐµÑÐ³Ð¸Ñ, Ð¿Ð¾ÑÐºÐ¾Ð»ÑÐºÑ Ð¾Ð½Ð¾ Ð¼Ð¾Ð¶ÐµÑ ÑÐ¿Ð¾ÑÐ¾Ð±ÑÑÐ²Ð¾Ð²Ð°ÑÑ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ½ÐµÑÐ³Ð¾ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ ÑÐ±Ð¾ÐµÐ² Ð² ÑÐ¸ÑÑÐµÐ¼Ðµ Ð·Ð´Ð°Ð½Ð¸Ð¹. Ð¦ÐµÐ»Ñ ÑÑÐ¾Ð¹ Ð´Ð¸ÑÑÐµÑÑÐ°ÑÐ¸Ð¸ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² ÑÐ¾Ð¼, ÑÑÐ¾Ð±Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸ÑÑ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ð° ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸. Ð­ÑÐ¸ Ð´Ð°Ð½Ð½ÑÐµ Ð²ÐºÐ»ÑÑÐ°ÑÑ Ð´Ð°Ð½Ð½ÑÐµ Ð¾ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ð¸ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸, Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ðµ ÑÐ¾Ð»Ð½ÐµÑÐ½Ð¾Ð¹ ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð¸ Ð¿Ð¾Ð³Ð¾Ð´Ð½ÑÑ ÑÑÐ»Ð¾Ð²Ð¸ÑÑ, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐ°Ð½ÐµÐµ ÑÐ¾Ð±Ð¸ÑÐ°Ð»Ð¸ÑÑ ÐµÐ¶ÐµÑÐ°ÑÐ½Ð¾ Ð² Ð¢ÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð¼ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑÐµ ÐÐ°Ð¿Ð¿ÐµÐµÐ½ÑÐ°Ð½ÑÐ°-ÐÐ°ÑÑÐ¸. ÐÐ½ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÐµÑÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ 36-ÑÐ°ÑÐ¾Ð²Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð³Ð¾ÑÐ¸Ð·Ð¾Ð½ÑÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð½Ð°Ð·ÑÐ²Ð°ÐµÐ¼Ð¾Ð¹ Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ¸ÑÑÐµÐ¼Ð¾Ð¹ Ð¼Ð½Ð¾Ð³Ð¾Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÑÑ ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð¿Ð°Ð¼ÑÑÐ¸, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾Ð´Ð½Ð¾Ð¹ Ð¸Ð· Ð»ÑÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. ÐÐ»Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¾ Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ð¸ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑÑÑ ÑÐ¾ÑÐ½ÑÐ¹ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð· Ñ ÐÐÐÐÐ Ð² ÑÐ°Ð·Ð¼ÐµÑÐµ 2,45%. ÐÑÐ¾Ð³Ð½Ð¾Ð· ÑÐ¾Ð»Ð½ÐµÑÐ½Ð¾Ð¹ ÑÐ½ÐµÑÐ³Ð¸Ð¸ Ð¾ÐºÐ°Ð·Ð°Ð»ÑÑ Ð¼ÐµÐ½ÐµÐµ ÑÑÐ¿ÐµÑÐ½ÑÐ¼, Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÐ² ÐÐÐÐÐ Ð² ÑÐ°Ð·Ð¼ÐµÑÐµ 55,86% Ð² Ð»ÑÑÑÐµÐ¼ ÑÐ»ÑÑÐ°Ðµ.
description: Buildings stock represented 41% of the final European energy consumption in 2016. It is, therefore, critical to improve their energy efficiency. Forecasting the electricity consumption of buildings would help users in saving energy, as it can support energy efficiency and reveal building system faults. The focus of this thesis is to apply machine learning for forecasting the electricity consumption and production. The data includes electricity consumption, solar power generation and weather times series that were previously collected hourly in the campus
of Lappeenranta-Lahti University of Technology. It is analysed over a 36-hour prediction horizon with a state-of-the-art model, called the Interpretable Multi-Variable Long Short-Term Memory Neural Network, that was selected among
the best forecasting models. An accurate forecast with a MAAPE of 2.45% is achieved for the electricity consumption data. Forecasting the solar power generation data was less successful, reaching a MAAPE of 55.86% at best.