Method for optimizing the consumption of renewable energy

Abstract

A method for optimizing consumption of electrical energy in a dwelling from renewable sources includes determining electrical energy consumption in the dwelling within a first time interval based on historical data; determining electrical energy production from the renewable sources in the first time interval based on historical data; entering, in an electronic control unit, one or more utilities present in the dwelling and for which to generate an activation and/or deactivation schedule in a second period of time after the first period of time; and generating the activation and/or deactivation schedule as a function of the historical data of energy consumption and production and forecasted weather data for the second time interval, so that the activation and/or deactivation schedule indicates, within the second time interval, a series of times and/or time sub-intervals distributed within the second time interval when to activate and/or deactivate one or more of the utilities.

Claims

1. A method for optimizing electrical energy consumption from renewable sources in a dwelling, the method comprising: determining electrical energy consumption in the dwelling over a first time interval based on historical data of electrical energy consumption over the first time interval; determining electrical energy production from renewable sources present and in use in the dwelling over the first time interval based on historical data of electrical energy production over the first time interval; entering, in a control unit, one or more utilities which are present in the dwelling and for which to generate an activation and/or deactivation schedule in a second time interval subsequent to the first time interval; and generating the activation and/or deactivation schedule of the one or more utilities in the second time interval, the activation and/or deactivation schedule being obtained as a function of at least: the historical data of energy consumption and production, and forecasted weather data related to the second time interval, whereby the activation and/or deactivation schedule indicates, within the second time interval, a series of times and/or time sub-intervals distributed in the second time interval in which the one or more of the entered utilities can be activated and/or deactivated.

2. The method according to claim 1, wherein the utilities comprise one or more of the following: one or more air conditioning systems; or one or more household appliances.

3. The method according to claim 1, wherein determining an electrical energy consumption in the dwelling comprises determining the electrical energy consumption with at least one first sensor that measures in real time the electrical energy consumption made, and wherein determining electrical energy production from renewable sources comprising determining the electrical energy production with at least one second sensor that measures in real time the electrical energy produced.

4. The method according to claim 3, wherein generating the activation and/or deactivation schedule comprises using a control unit that receives, as input, data coming from the at least one first and the second sensors and forecasted weather data, the control unit processing the input data according to a specific function.

5. The method according to claim 1, wherein generating the activation and/or deactivation schedule comprises using a control unit that receives, as input, the historical data of electrical energy consumption and production and the forecast weather data, the control unit processing according to a specific function the input data.

6. The method according to claim 1, wherein generating the activation and/or deactivation schedule providing access to one or more utilities with a control panel communicating with a control unit or through a cloud connection with a virtual control unit via a mobile device or a computer.

7. The method according to claim 1, wherein the activation and/or deactivation schedule is visible on a screen and indicates, based on a set time interval, a time slot and day on which to activate and/or deactivate predetermined utilities.

8. The method according to claim 1, wherein the historical data of electric energy consumption and production are provided continuously or at predetermined time intervals.

9. The method according to claim 1, further comprising the step of subtracting, from the function of the historical data of energy consumption, consumption patterns only to utilities entered in a control unit and whose schedule is to be generated, the function overlapping with an energy production function dependent on the historical data of the energy production and on the forecasted weather data so as to generate, from an overlap of the two functions, the activation and/or deactivation schedule as a function of energy surplus areas.

10. An assembly for optimizing electrical energy consumption coming from renewable sources in a dwelling, the assembly comprising: a first sensor adapted to detect the electrical energy consumptions in real time of one or more utilities set up for use in the dwelling; and a second sensor adapted to detect in real time energy production of renewable energy sources set up for use in the dwelling; a control unit communicating with the first and the second sensor so as to receive, as input, data measured by the first and the second sensors, the control unit further receiving, as input, weather data; wherein the control unit is programmed to calculate, based on the data received as input from the first and the second sensors, historical reference data of electrical energy consumption and electrical energy production from the renewable sources within a first time interval in which the data from the first and the second sensors were acquired, wherein the control unit is further programmed to acquire, as input, data related to the one or more utilities which are present in the dwelling and of which to generate an activation and/or deactivation schedule over time, wherein the control unit is further programmed to generate the activation and/or deactivation schedule of the one or more utilities in a second time interval subsequent to the first time interval, and wherein the activation and/or deactivation schedule is produced as a function of: the historical reference data of electrical energy consumption and production, and forecasted weather data related to the second time interval for which to generate the activation and/or deactivation schedule, whereby the activation and/or deactivation schedule indicates, within the second time interval, a series of times and/or time sub-intervals distributed in the second time interval in which to be able to activate and/or deactivate one or more of the one or more utilities.

11. The assembly according to claim 10, wherein the control unit estimates the times and/or time sub-intervals having an energy surplus, and wherein, based on the calculated energy surplus and the consumptions of the one or more utilities, the control unit provides a report one which of the one or more utilities to activate and/or deactivate in the second time interval.

12. The assembly according to claim 10, wherein consumption patterns related only to the one or more utilities which have been entered in the control unit and whose schedule is to be generated are subtracted from the historical reference data of the electrical energy consumption.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The invention, in one or more of the embodiments thereof, will be described below in accordance with the following drawings:

[0084] FIG. 1 schematically shows a dwelling 100, therefore, a local unit, with one or more photovoltaic panels (FV) that produce a certain energy quantity schematized with the storage battery. FIG. 1 also shows an electrical outlet to indicate the mains energy; and schematizes some household appliances (200, 300) and the sensor (CG) that reads and detects the consumptions of the dwelling in real time;

[0085] FIG. 2 schematizes the photovoltaic panels (FV) irradiated by solar rays and, therefore, in energy production condition. FIG. 2 also schematizes the sensor (PF) that detects energy production in real time;

[0086] FIG. 3 schematizes the control unit that receives as input the measurements of the sensors (CG) and (PF);

[0087] FIG. 4 and FIG. 5 schematize a graph to highlight the historical data (historical_data) of energy consumption and production that are processed by the control unit as a function of the input data received through the measurements of the sensors (CG) and (PF); and show, in particular, on a Cartesian graph, reference historical data until a time t0, which is then followed by the calculation after time t0 which is based, in part, on the historical data (the part subsequent to to indicated as a forecast);

[0088] FIG. 6 further schematizes the control panel through which all the utilities and their specifications can be manually entered (e.g. dimensions/powers, etc.); and shows how those data are transferred to the control unit that has processed the historical data of energy consumption and production and that further receives weather data as input in order to elaborate a schedule of the uses by the household appliances according to a specific forecast time program that is a function of said input data, i.e., the utilities entered, the historical data and the weather data;

[0089] FIG. 7 schematizes in a graph an example of one-day programming highlighting an area of consumptions estimated historically over a time frame and, in the same time frame, a graph of the renewable energy produced historically together with the correction of the weather, so as to highlight with solid color the area of energy surplus that one can have in specific time intervals; accordingly, knowing the household appliances that one wants to use, in particular those entered in the control panel, and their consumptions, the processor can implement a time program in a specific period of time indicating if and which household appliances to use and the recommended time of use, in order to make the most and best use of the energy surplus. Therefore, if some household appliances experience an excessive consumption in the specific time interval, the program selects the use of other appliances entered in the list that are compatible with the energy surplus;

[0090] FIGS. 7A and 7B show a few graphs suitable for understanding an implementation of the calculation that is performed.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

[0091] The invention relates to an innovative method of generating a forecast of energy production by domestic renewable energy sources and to a forecast of energy consumption within a building. Based on this forecast, a system can calculate the best way to use household appliances within the forecast window, so as to maximize the self-produced consumption of renewable energy.

[0092] A forecasting system according to the invention has the great advantage of enabling a user to have in advance an estimate of times and/or days in which to carry out certain operations, so allowing him/her to be able to best manage himself/herself also based on his/her plans and presence in person at home.

[0093] In FIG. 1, a dwelling 100 is schematized having, by way of an example, energy accumulated by solar panels (FV) in addition to energy that is instead related to the normal mains electricity grid.

[0094] As schematized in FIG. 1, a part of the energy is therefore accumulated from such renewable sources, i.e., the photovoltaic panels (FV) while another part of the energy can come from and is available from the normal electricity grid.

[0095] The household appliances and all the domestic utilities have a predetermined consumption.

[0096] FIG. 1 also schematizes some household appliances including, by way of a non-limiting example, a washing machine 200 and a dishwasher 300.

[0097] In accordance with the invention, there is a first sensor (GC) which is connected, and therefore communicating, with the domestic utilities. The sensor (CG) is therefore configured to detect and/or store consumption by the utilities.

[0098] The utilities shown in the schematization of FIG. 1 are obviously only a non-exhaustive and non-limiting example.

[0099] In FIG. 2, the dwelling is also provided with one or more renewable energy production systems (FV) including, by way of a non-limiting example, photovoltaic panels (FV) (one or more than one).

[0100] It is clear that, in the invention, the photovoltaic panel is only an example since the dwelling can in fact include other systems as an alternative or in addition to the panels, for example, wind systems.

[0101] In all cases, regardless of how many and which renewable energy production systems are present, in accordance with the invention there is a sensor (PF) which is connected to such renewable energy production systems in order to detect and/or store in real time the energy production that is actually made.

[0102] The system therefore makes it possible to monitor and maximize consumption that comes from the renewable sources.

[0103] In particular, the system can be specifically programmed to perform certain calculation operations that allow a forecast to be made over a specific time frame.

[0104] Assuming, therefore, the presence of the aforementioned photovoltaic panels (FV), these are connected to the sensor (PF) to detect and thus measure photovoltaic production in real time (and/or from any other renewable sources if present).

[0105] The sensor (CG) is arranged to detect the overall consumption of the building in real time.

[0106] In a first step, historical data related to the consumptions are extrapolated, taking advantage of the real-time measurements detected by the sensor (GC) and, also, historical data related to the renewable energy produced through the real-time measurements of the sensor (FV).

[0107] FIG. 3 schematizes a control unit.

[0108] The control unit, as is known in the art, is provided with a specific programmed or programmable processor.

[0109] The processor may be specifically programmed in accordance with the following description.

[0110] As schematized by FIG. 3, the control unit (also identified here simply as unit) receives measurement data in real time coming from the sensors, in particular from the sensor (CG) that detects in real time the consumptions of the dwelling and from the sensor (FV) that detects the amount of renewable energy produced by the energy systems installed in the dwelling (for example solar panels, wind energy and/or, more generally, other systems).

[0111] In a first step, therefore, the processor can create a log of the consumptions and a log of the energy produced in a specific time interval (t0).

[0112] More particularly, as schematized in FIG. 4, the real-time measurements of the sensor (CG) allow the processor to create a data-log highlighting a consumption over a specific time frame, for example estimated over one week (historical_data_1). The data-log therefore contains, instant by instant, in the predefined time frame, a log of energy consumption.

[0113] The methodologies for calculating an average consumption, based on the real-time measurement data coming from the sensor (CG), can be different and the person skilled in the art will be able to implement the calculation algorithm that he/she deems the most suitable.

[0114] In any case, as schematized in FIG. 4, a data-log of data is then extrapolated indicating a consumption trend measured over a specific time frame, for example, as said, one week.

[0115] Obviously, although not schematized in the figure, the consumption measurement can be calculated precisely over any time frame.

[0116] From the schematization of FIG. 4 it is therefore evident, by way of example, that a calculation of consumptions in a week frame can be made in which some peaks show an extra measurement of consumption linked to a certain life pattern of the subject, for example because at certain moments of the day he/she uses more specific household appliances.

[0117] Similarly, as per the schematization in FIG. 5, the control unit performs a measurement of energy production by the renewable sources in a predetermined time frame, preferably over the same previous time interval (historical_data_2).

[0118] So, historical data of energy consumption and production can be determined in a specific historical time interval which, preferably, is the same for both of the aforementioned measurements.

[0119] In particular, the data-log [historical_data_1] indicates, instant by instant, an energy consumption calculated over a certain time frame (0-t0) while the data-log [historical_data_2] indicates instant by instant the calculation of the historical energy production of the renewable sources in the same time frame (0-t0).

[0120] Historical data_2 and historical data_1 also take into account, of course, the structural parameters of the utilities and renewable energy sources.

[0121] On the basis of said generated historical data, a forecast of the future instantaneous consumption values over a settable time horizon, for example one week, is then generated.

[0122] FIG. 4, as said, schematizes a graph of instantaneous consumptions that has been hypothesized by the processor on the basis of the data extrapolated by the sensor (CG).

[0123] The sensor PF, in turn, is the sensor that reads the consumptions related to the renewable source, such as for example one or more photovoltaic panels, from which the estimate of FIG. 5 follows. It is therefore possible to create, also in this case and as schematized in FIG. 5, a time horizon of instantaneous values of production of current by the system(s) in question. This can take into account the structure and the type of plant in question (therefore, for example, number and power dimensions of the solar panels).

[0124] For example, the time horizon for the historical calculation can be one week.

[0125] In this way, as schematized by FIGS. 4 and 5, there are two temporal calculations based on historical data.

[0126] One is that of the building's consumption and, therefore, how much the utilities generally consume over a certain time frame, and the other one is how much the renewable energy production over a fixed time frame is, for example one week.

[0127] A particularly innovative element of the invention consists further in having the calculation system (therefore the calculation function as a whole) integrated and taking into consideration meteorological forecasts including, for example sun, winds and/or temperatures.

[0128] In fact, it is known that the solar panels produce energy as a function of the solar rays from which they are hit. It is therefore known that energy production is strongly influenced by the weather conditions since a rainy day leads to a very low energy production compared to a hot and sunny day condition.

[0129] The same is true for the wind, when wind systems are present among the renewable energy production systems.

[0130] Therefore, the calculation algorithm that generates the consumption program, i.e., the consumption schedule, is based not only on the previous historical data inherent in energy consumptions and production, as indicated above, but also as a function of weather estimates.

[0131] The control unit can therefore be connected, for example through the interne, to weather information sources that in real time continuously or at predetermined time intervals, send parameters correlated to the presence or absence of the sun, the temperatures and the presence or absence of wind as well as any other detectable weather parameters (for example humidity).

[0132] The calculation algorithm therefore takes these parameters into account, which acquire a specific weight in the calculation function.

[0133] When, for example, it is envisaged in the time frame for which the calculation of the program is carried out (for example one week) having sunny weather and strong winds, an estimate of a consumption program can be made with greater precision using, for example, corrective factors that can be entered in the formula of the function and that result in an increase of the available energy possibilities. On the contrary, low winds and/or rain or overcast sky lead(s) to a corrective factor that decreases energy availability, thereby modifying the program.

[0134] The calculation function can be any and a person skilled in the art will be able to find the one most suitable one for his/her needs.

[0135] FIG. 6 schematizes inputs and outputs.

[0136] Starting from forecasts not only of the weather but also of energy consumptions and production as introduced above, the processor can now generate a Consumption Schedule over a set time horizon, for example one week and after the time interval on which the historical estimate was made.

[0137] In particular, it is possible to enter, for example from the control panel, all the utilities present in the dwelling with their relative consumption.

[0138] FIG. 6 schematizes a control panel normally present for this type of system.

[0139] It is managed electronically and is provided with a panel through which to read the data and/or a corresponding push-button panel for data entry.

[0140] The panel, therefore, allows entering the utilities in general (e.g., refrigerator, iron, etc.).

[0141] The control panel communicates with the control unit that processes the estimate calculation.

[0142] In particular, FIG. 6 therefore shows the utilities from 1 to n that can be entered in the control panel and of which a program of use over time is to be drawn up.

[0143] More particularly, the relative consumptions of each utility can be entered.

[0144] For example, the control panel can already envisage a list of utilities, divided by brand and model, which can be selected by the user and which, therefore, already contain as preset all the consumption values necessary for the above-described calculation.

[0145] The user can then select, for example, his/her type of air conditioner that he/she has installed, his/her refrigerator etc. by checking it from a predefined list.

[0146] Said consumption data of the entered utilities are therefore acquired by the related control unit with its processor which, through the historical estimates that were previously introduced, can easily implement a consumption forecast calculation.

[0147] In particular, the historical data processed and the weather data together with the characteristics of the entered utilities allow the creation of a calculation function.

[0148] The invention therefore includes a strategy, implemented by a control unit present within the dwelling, which has as output a consumption schedule over a defined and settable time horizon.

[0149] An example of a consumption schedule (of one week, generated on Sunday for the subsequent week) can for example be:

[0150] Monday:

[0151] at 8:34 a.m. switching on air conditioner in the living room

[0152] at 9:02 a.m. switching on washing machine

[0153] at 9:58 a.m. switching on air conditioner in the kitchen

[0154] at 10:45 a.m. switching off washing machine

[0155] at 10:56 a.m. switching on tumble dryer

[0156] at 12:03 p.m. switching off tumble dryer

[0157] at 2:34 p.m. switching off air conditioner in the living room

[0158] at 3:25 p.m. switching off air conditioner in the kitchen

[0159] Tuesday:

[0160] at 8:43 a.m. switching on air conditioner in the living room

[0161] at 9:24 a.m. switching on washing machine

[0162] at 9:54 a.m. switching on air conditioner in the kitchen

[0163] at 10:23 a.m. switching off washing machine

[0164] at 10:44 a.m. switching on washing machine

[0165] at 11:56 a.m. switching off washing machine

[0166] at 12:43 a.m. switching on tumble dryer

[0167] at 13:42 p.m. switching off tumble dryer

[0168] at 3:24 p.m. switching off air conditioner in the living room

[0169] at 4:45 p.m. switching off air conditioner in the kitchen

[0170] and so on until Sunday.

[0171] In fact, knowing a consumption in the dwelling and knowing a production of renewable energy over a time frame, it is easy, as per schematization in FIG. 7, to identify the areas where there may be an energy surplus related to energy production from renewable sources as a function of weather conditions and areas where, instead, the amount produced from renewable sources is lower than the value of actual consumption. From this, consequently, the processor can give as output a consumption program aimed at optimizing, by time slots, the consumption coming from renewable sources, being statistically known, as a function of historical consumptions, the historical average quantity of renewable energy produced.

[0172] The graph of FIG. 7, in two different colors, shows by way of example the line that delimits the area of the estimated consumptions and with an orange color the line that delimits the area of produced energy influenced by weather conditions, indicating with solid color the areas of energy surplus produced by renewable energies with respect to consumptions.

[0173] The example of FIG. 7 shows an estimate for a day but the estimate can be made over a longer time, generally a time compatible with the program that one wishes to generate, for example one week if the program that one wishes to implement runs from week to week.

[0174] Still in accordance with FIG. 7, some traits, called sub-delta, in which there is an energy surplus, are highlighted in solid color. FIG. 7 shows three of them by way of example and in the first one, by way of example, an energy surplus is shown lasting six hours.

[0175] In this way, the program can estimate, instant by instant, the time intervals in which there is an energy surplus, which is also quantified as the difference between consumption and energy produced.

[0176] Therefore, when the consumptions of the entered household appliances is known and other parameters are also known for some of them, such as operating time as in the case of a dishwasher, the system can estimate if and which ones among the household appliances can be used and which, instead, are to be deactivated in accordance with the program example that was provided above.

[0177] This way, the user has in advance a program of times and days (for example, a weekly program) in which he/she knows that he/she can switch on certain household appliances and/or switch off others and can, therefore, follow the provided schedule both manually and remotely.

[0178] Such schedule can be carried out manually in all dwellings where the utilities require an in-person activation (for example, in a washing machine that must be pre-loaded with the clothes to be washed, the detergent must be inserted and only after these steps it can be started remotely).

[0179] Additional utilities, such as air conditioners, can also be controlled via the app at a distance (therefore remotely), therefore, a communicative interface with this system can allow an automatic activation of all controllable utilities at a distance (remotely) according to the indicated program.

[0180] If, for example, in a weekly programming frame that has been provided the user checks which are the days and times of recommended activation of the washing machine, he/she can safely, from time to time, prepare in advance the load and then enable the program, remotely, to activate the washing machine.

[0181] The same applies for the dishwasher or other utilities in general.

[0182] As mentioned, the user can see the consumption schedule on a display, when provided, on the control panel of the control unit or through an app usable from a computer such as PC or a mobile device.

[0183] The user can also decide whether to use the consumption schedule as a guideline, manually implementing the scheduling, or allow the control unit to act for him/her, which will switch on and off the devices directly by communicating with them through an electronic remote control.

[0184] The forecast-based system, in addition to the previously discussed advantage of transmitting awareness to the user of what the future activations will be and when they will occur, bases the scheduling on optimization over an extended time horizon.

[0185] To clarify the preceding description, it should be mentioned that the historical data is implemented from time to time.

[0186] The historical consumption data can easily identify exactly the consumption habits of the user, as the consumption patterns clearly indicate which household appliance or utility is consuming and how much and for how long. The historical data, therefore, in addition to detecting consumption can easily provide information on the type of household appliance or utility in general that consumes, and how often consumption occurs.

[0187] In other words, the forecast program is based on historical data which, automatically, provides the number of activations/deactivations of the utilities and generates a program that is based and allocates those utilities most efficiently according to their frequency of use.

[0188] For example, the historical data can detect how many washings can be done in a week (for example twenty) and therefore the program allocates said twenty washings in the most efficient manner.

[0189] This is because the analysis of the consumptions allows highlighting consumption patterns that are easily identifiable utility by utility, which are different from one another.

[0190] Therefore, the log can also be used to replicate a program of use according to the detected historical frequency.

[0191] It can be seen that the historical data is updated with a periodic frequency or continuously since the historical data changes both as a function of weather data and as a function of the habits of the user. Therefore, the function that estimates and generates the schedule of use is a function that acquires as input historical data that from time to time are different and can be updated periodically.

[0192] By automatically acquiring, for example, statistical data indicating that the user needs about twenty washings per week, the system will concentrate the greatest occurrence near the days when greater production is expected. In regard to air conditioning, which alone takes up about 80% of the energy consumption of a dwelling, the advantage of working with forecasts is tangible and derives from being able to forecast and compensate for the days of low production with the days of greater production, for example: tomorrow it is sunny, in two days it will be rainy and in three days it will be sunny again. The system, by way of scheduling, will then provide for a greater accumulation in its program on the sunny days in order to, for example, recommend a use of the energy accumulated on a rainy day.

[0193] As previously discussed, in order to optimize accuracy, the above indicated statistical calculation based on consumption and on energy production can be repeated from time to time in so as to improve accuracy.

[0194] This is not only due to the fact that some habits can change over time but also as a function of the seasons where, obviously, there is greater and/or lesser consumption as well as greater and/or lesser energy production.

[0195] For example, in the summertime there is a greater consumption of air conditioning but there is also a greater accumulation of solar energy.

[0196] Reactivating each time, in predetermined time intervals, a repetition of the calculation for the estimation of the historical reference data provides for more precise estimations.

[0197] FIGS. 7A and 7B show an example of a calculation.

[0198] Starting from a calculation of the consumption (historical_data_1) in a specific period of time (0-t0), the system recognizes the consumption patterns entered in the control panel and from which to make the schedule, for example, the washing machine or other household appliances, as these have repeated occurrences over time which can be easily traced back, for example, using brands and models of the utilities.

[0199] It should also be noted that the control panel allows the entry of only and exclusively some utilities, i.e., those and only those whose consumption schedule is to be made while other utilities cannot be entered because they have an unpredictable usage or in any even a usage that cannot be programmed.

[0200] For example, in accordance with the invention, kitchen household appliances such as oven and/or induction cookers for the kitchen cannot be entered, as well as hair dryers.

[0201] That is because it is not practically possible to force a user to cook at a time at night or to dry his/her hair at night.

[0202] Some utilities are therefore excluded.

[0203] The subtraction of certain patterns is therefore only for those utilities entered by the user, in order to develop an effective program or schedule.

[0204] FIG. 7A, in step 2, shows the search and the recognition of the exemplary pattern of the washing machine and in any case of all and of only those patterns related to the specific utilities entered in the control panel and of which a schedule is to be made.

[0205] Step 3 of FIGS. 7A and 7B shows the forecast in the case of no pattern subtraction (incorrect case) and in the case of pattern subtraction (correct case).

[0206] If the recurring pattern (in the example of the washing machine) is not subtracted from the historical data of consumption, the final forecast (also a function of the historical estimate of the energy produced and weather data) is not true and would therefore lead to a forecast such as that of FIG. 7A step 3 with consequent calculation of the consumptions and produced energy, as a function also of weather forecast, which would carry the risk of presenting no possible free allocation area (see FIG. 7B Schedule built on the wrong forecast (without pattern subtraction). FIG. 7B Schedule built on the wrong forecast (without pattern subtraction) shows in fact the calculated line of the consumptions and that of the produced energy, which is also dependent on weather conditions and the two curves almost overlap, thus indicating that is would be impossible to allocate the use of the utilities.

[0207] The correct procedure, therefore, provides, from the historical estimate of consumption, for the subtraction of the patterns which are related to the utilities entered in the control panel and of which to generate the program (in the example of the figure only the washing machine, but that could be a washing machine and other utilities).

[0208] FIG. 7A, step 2, shows such subtraction.

[0209] At this point, as shown in FIG. 7B case Schedule built on correct forecast (with pattern subtraction) a correctly lower consumption line is highlighted compared to a final energy production line that is a function of the historical data but also of the weather forecast.

[0210] Having subtracted the consumptions linked to the utilities for which the schedule is to be made, the calculated consumptions are only the historical ones actually carried out by all and only those utilities not entered in the control panel because not of interest to the user (he/she does not want to enter them) or that cannot be entered no matter what. In fact, some utilities cannot be entered, in particular all those for which a program of use cannot be made such as the utilities for cooking, hair dryer etc.

[0211] The final result of the two overlapping graphs is, in this case, the presence of areas with a possible energy surplus (a function of weather conditions), within which to allocate the consumption of the entered utilities in order to power them as much as possible with renewable energy sources.

[0212] In the example of FIG. 7B there are indicated the free areas which have been calculated and which would be big enough to contain an example pattern_1.

[0213] This implies, as described above, that the activations/deactivations of the utilities entered in the control panel are allocated as a function of the dimensions of the calculated free areas (amount of energy surplus), which must be compatible (as can be inferred from FIG. 7B) with the known consumption by those utilities, As mentioned previously, an a priori consumption is known for each utility and such consumption is subtracted from the historical data.

[0214] Therefore, because the consumptions are known of the entered utilities, those are allocated in the most suitable surplus areas.

[0215] As can be inferred from FIG. 7B, said activation and/or deactivation schedule is a function of the areas of energy surplus and function of the known consumptions of the utilities. This way, the system appropriately allocates the activations of each utility in the most suitable areas.

[0216] Without prejudice to all of the above, in any case, the calculation function will best allocate the switching on/off times of the utilities so that they are powered as much as possible by the renewable sources.

[0217] Obviously, optimization can also result in a partial power supply with mains current but, in any case, everything described so far is aimed at best maximizing the use of energy from renewable sources.

[0218] The remotely controllable electronic household appliances can be of various kinds, such as washing machines, dishwashers, water heaters, air conditioners, heat pumps, but in general the common thread is that all these devices must produce a result within a predefined time. However, there are also devices that cannot be included in the consumption scheduling strategy, and those are the devices whose use has an intrinsic component of randomness at the time of need, such as television (and in general all devices in the entertainment area), oven, or cooking hob. One of the aspects of the present invention is the maintenance or even the improvement of comfort, and for this reason, the use program for the utilities preferably excludes some household appliances such as an electric oven, a hair dryer and all those utilities that have a random use that cannot be regulated as a matter of fact.

[0219] In all the configurations of the present invention, without prejudice to all that has been described, the control unit may not be installed directly in the dwelling but may be in the cloud.

[0220] In this case, the control unit may be in the form of a server reachable on the Internet and with the sensors that send data to that specific server that carries out the above-described processing.

[0221] In this case, the remote control unit, in the cloud, may be a virtualized control panel through mobile apps or other similar systems such as computer programs.

[0222] The system in all of the above-described configurations, in particular in the latter with a virtual control unit, lends itself well to an application (App) for mobile devices such as mobile telephony devices.

[0223] In the present description, the term dwelling means a building or a room in general for house and/or office use or rooms in general for industrial use (as in a factory), for warehouse use, and/or for commercial use and in any case any type of building/construction/room in the broad sense without any limitations as long as it has renewable energy sources and utilities that generate electrical consumption.

CAPTIONS

[0224] (FV)=Photovoltaic panels [0225] (CG)=Sensor to detect total consumption of the building in real time [0226] (PF)=Sensor to detect photovoltaic production of the building in real time [0227] (L)=A household appliance (e.g., a washing machine) that can be controlled electronically remotely [0228] (C)=A control unit that implements and executes the efficiency logics.